{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "056f4078",
   "metadata": {},
   "source": [
    "## Intervening on subcomponents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ee206766",
   "metadata": {},
   "outputs": [],
   "source": [
    "__author__ = \"Zhengxuan Wu\"\n",
    "__version__ = \"11/15/2023\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13019abe",
   "metadata": {},
   "source": [
    "### Overview\n",
    "\n",
    "This tutorial shows how you can intervene at specific position within representations of a specific head. This is sort of nested interventions where you choose a head to intervene first, and then you choose a specific location, or multiple locations. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67b92d31",
   "metadata": {},
   "source": [
    "### Set-up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dcc513c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    # This library is our indicator that the required installs\n",
    "    # need to be done.\n",
    "    import pyvene\n",
    "\n",
    "except ModuleNotFoundError:\n",
    "    !pip install git+https://github.com/stanfordnlp/pyvene.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aefcde00",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pyvene import embed_to_distrib, top_vals, format_token\n",
    "from pyvene import (\n",
    "    IntervenableModel,\n",
    "    VanillaIntervention,\n",
    "    RepresentationConfig,\n",
    "    IntervenableConfig,\n",
    ")\n",
    "from pyvene import create_gpt2\n",
    "\n",
    "%config InlineBackend.figure_formats = ['svg']\n",
    "from plotnine import (\n",
    "    ggplot,\n",
    "    geom_tile,\n",
    "    aes,\n",
    "    facet_wrap,\n",
    "    theme,\n",
    "    element_text,\n",
    "    geom_bar,\n",
    "    geom_hline,\n",
    "    scale_y_log10,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61544aed",
   "metadata": {},
   "source": [
    "### Factual Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4575c0bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loaded model\n",
      "The capital of Spain is\n",
      "_Madrid              0.10501234978437424\n",
      "_the                 0.0949699655175209\n",
      "_Barcelona           0.0702790841460228\n",
      "_a                   0.04010068252682686\n",
      "_now                 0.02824278175830841\n",
      "_in                  0.02759990654885769\n",
      "_Spain               0.022991720587015152\n",
      "_Catalonia           0.018823225051164627\n",
      "_also                0.018689140677452087\n",
      "_not                 0.01735665090382099\n",
      "\n",
      "The capital of Italy is\n",
      "_Rome                0.15734916925430298\n",
      "_the                 0.07316355407238007\n",
      "_Milan               0.046878915280103683\n",
      "_a                   0.03449810668826103\n",
      "_now                 0.03200329467654228\n",
      "_in                  0.02306535840034485\n",
      "_also                0.02274816483259201\n",
      "_home                0.01920313946902752\n",
      "_not                 0.01640527881681919\n",
      "_Italy               0.01577090471982956\n"
     ]
    }
   ],
   "source": [
    "config, tokenizer, gpt = create_gpt2()\n",
    "\n",
    "base = \"The capital of Spain is\"\n",
    "source = \"The capital of Italy is\"\n",
    "inputs = [tokenizer(base, return_tensors=\"pt\"), tokenizer(source, return_tensors=\"pt\")]\n",
    "print(base)\n",
    "res = gpt(**inputs[0])\n",
    "distrib = embed_to_distrib(gpt, res.last_hidden_state, logits=False)\n",
    "top_vals(tokenizer, distrib[0][-1], n=10)\n",
    "print()\n",
    "print(source)\n",
    "res = gpt(**inputs[1])\n",
    "distrib = embed_to_distrib(gpt, res.last_hidden_state, logits=False)\n",
    "top_vals(tokenizer, distrib[0][-1], n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b65ff4b8",
   "metadata": {},
   "source": [
    "### Patch Patching on Position-aligned Tokens with in Specific Head\n",
    "We path patch on two modules on each layer:\n",
    "- [1] MLP output (the MLP output will be from another example)\n",
    "- [2] MHA input (the self-attention module input will be from another module)\n",
    "\n",
    "Different from the basic tutorial, this tutorial intervenes on specific locations within specific heads. For instance, we want to intervene on the last token in head 4 but not other heads.\n",
    "\n",
    "**To do this, we need to tweak a little when we setup the intervention config.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "898907c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def position_in_head_config(model_type, intervention_type, layer):\n",
    "    config = IntervenableConfig(\n",
    "        model_type=model_type,\n",
    "        representations=[\n",
    "            RepresentationConfig(\n",
    "                layer,  # layer\n",
    "                intervention_type,  # intervention type\n",
    "                \"h.pos\",  # intervention unit is now [pos] within [h]\n",
    "                1,  # max number of unit\n",
    "            ),\n",
    "        ],\n",
    "        intervention_types=VanillaIntervention,\n",
    "    )\n",
    "    return config\n",
    "\n",
    "\n",
    "base = tokenizer(\"The capital of Spain is\", return_tensors=\"pt\")\n",
    "sources = [tokenizer(\"The capital of Italy is\", return_tensors=\"pt\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dcda628a",
   "metadata": {},
   "outputs": [],
   "source": [
    "target_head = 0\n",
    "\n",
    "# should finish within 1 min with a standard 12G GPU\n",
    "tokens = tokenizer.encode(\" Madrid Rome\")\n",
    "\n",
    "data = []\n",
    "for layer_i in range(gpt.config.n_layer):\n",
    "    config = position_in_head_config(\n",
    "        type(gpt), \"head_attention_value_output\", layer_i\n",
    "    )\n",
    "    intervenable = IntervenableModel(config, gpt)\n",
    "    for pos_i in range(len(base.input_ids[0])):\n",
    "        _, counterfactual_outputs = intervenable(\n",
    "            base,\n",
    "            sources,\n",
    "            {\n",
    "                \"sources->base\": (\n",
    "                    [[[[target_head]], [[pos_i]]]],  # intervene w/ target_head's pos_i\n",
    "                    [[[[target_head]], [[pos_i]]]],  # intervene on target_head's pos_i\n",
    "                )\n",
    "            },\n",
    "        )\n",
    "        distrib = embed_to_distrib(\n",
    "            gpt, counterfactual_outputs.last_hidden_state, logits=False\n",
    "        )\n",
    "        for token in tokens:\n",
    "            data.append(\n",
    "                {\n",
    "                    \"token\": format_token(tokenizer, token),\n",
    "                    \"prob\": float(distrib[0][-1][token]),\n",
    "                    \"layer\": f\"ov{layer_i}\",\n",
    "                    \"pos\": pos_i,\n",
    "                    \"type\": \"head_attention_value_output\",\n",
    "                }\n",
    "            )\n",
    "\n",
    "    config = position_in_head_config(\n",
    "        type(gpt), \"head_value_output\", layer_i\n",
    "    )\n",
    "    intervenable = IntervenableModel(config, gpt)\n",
    "    for pos_i in range(len(base.input_ids[0])):\n",
    "        _, counterfactual_outputs = intervenable(\n",
    "            base,\n",
    "            sources,\n",
    "            {\n",
    "                \"sources->base\": (\n",
    "                    [[[[target_head]], [[pos_i]]]],  # intervene w/ target_head's pos_i\n",
    "                    [[[[target_head]], [[pos_i]]]],  # intervene on target_head's pos_i\n",
    "                )\n",
    "            },\n",
    "        )\n",
    "        distrib = embed_to_distrib(\n",
    "            gpt, counterfactual_outputs.last_hidden_state, logits=False\n",
    "        )\n",
    "        for token in tokens:\n",
    "            data.append(\n",
    "                {\n",
    "                    \"token\": format_token(tokenizer, token),\n",
    "                    \"prob\": float(distrib[0][-1][token]),\n",
    "                    \"layer\": f\"v{layer_i}\",\n",
    "                    \"pos\": pos_i,\n",
    "                    \"type\": \"head_value_output\",\n",
    "                }\n",
    "            )\n",
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fa7273a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 640
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "df[\"layer\"] = df[\"layer\"].astype(\"category\")\n",
    "df[\"token\"] = df[\"token\"].astype(\"category\")\n",
    "nodes = []\n",
    "for l in range(gpt.config.n_layer - 1, -1, -1):\n",
    "    nodes.append(f\"ov{l}\")\n",
    "    nodes.append(f\"v{l}\")\n",
    "df[\"layer\"] = pd.Categorical(df[\"layer\"], categories=nodes[::-1], ordered=True)\n",
    "\n",
    "g = (\n",
    "    ggplot(df)\n",
    "    + geom_tile(aes(x=\"pos\", y=\"layer\", fill=\"prob\", color=\"prob\"))\n",
    "    + facet_wrap(\"~token\")\n",
    "    + theme(axis_text_x=element_text(rotation=90))\n",
    ")\n",
    "print(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7730ab69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABQAAAAPACAYAAABq3NR5AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/OQEPoAAAACXBIWXMAAB7CAAAewgFu0HU+AACqEklEQVR4nOzde5zddX3v+/eaFUhmIiODBkFDKNdqARW1FjgW0Y1i5SJeSqnS6hbcfej2/tg9PS1Ke9DywNPuXXdxoz3aqhWU7iKKXJRaLcGtRo7VoHgLUWiC4RYyZCBMBrOyzh/sTImZhEmyZn3z/a3n8/Hgwaz53V7rx0+JH39r/VrdbrcbAAAAAKCRhkoHAAAAAABzxwAQAAAAABrMABAAAAAAGswAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAabF7pAPpjxYoVpRMAAAAA2E1HHnnkTm/jDkAAAAAAaDADQAAAAABoMANAAAAAAGgwA0AAAAAAaDADQAAAAABoMANAAAAAAGgwA0AAAAAAaDADQAAAAABoMANAAAAGzjvf+c686EUvyic+8Yld3sfZZ5+dF73oRfnSl76009suX748L3rRi/KiF71ol48PADBb80oHAABQt0984hP55Cc/Of36r/7qr/LsZz97u+tv3Lgxr3nNa7Jhw4YkybOe9ax88IMfnONKAIDB5Q5AAAB66oYbbtjh8q997WvTw7+aPfWpT81BBx2UhQsXlk4BANghdwACANATT37ykzM+Pp6bbrop73jHO7JgwYIZ19vykdmnPOUpueeee/qZ2FP/7b/9t9IJAACz4g5AAAB64olPfGKe//zn5+GHH85NN9004zr33ntvli9fntHR0fzGb/xGnwsBAAaTOwABAOiZU045Jd/85jdzww035KUvfek2y//pn/4pmzdvzn/4D/8hQ0Pb//+iN27cmK997Wv51re+ldtvvz333XdfNm7cmP322y/PfOYz85rXvCZHHnnkDltuvPHGXHXVVVm5cmWGhobyK7/yK3nFK16Rl7zkJTvcbsuDOf7qr/4qT3va03LZZZfl//v//r+sXbs2S5Ysycc+9rEkjz4E5J577skf/dEf5WUve9k2+xkfH88nP/nJfPOb38z4+HjGxsby/Oc/P7//+7+/w+MDAPSaASAAAD1z/PHHZ5999sny5ctz7733Zv/9999q+ZbvBzzllFPy5S9/ebv7ufHGG/OBD3wgSTI0NDT9PXv33HNPvvzlL+erX/1q/s//8/+ccciYJP/jf/yPXHnllUmSVquVhQsX5kc/+lF+8IMfZOXKlbN6L6tXr86f/dmfZf369VmwYEHa7fastkuSVatW5V3velfWrVuXJJk/f34mJiZy7bXX5utf/3rOPffcWe8LAGB3GQACANAze++9d170ohflC1/4Qv7pn/4p55xzzvSyW2+9NXfeeWcOPvjg/Oqv/uoOB4BPeMITcvbZZ+c3f/M3c/jhh2fvvfdOt9vNnXfemU996lP58pe/nP/6X/9rnvWsZ+UpT3nKVtt+9atfnR7+nXHGGfmP//E/Zt99983ExEQuu+yy/M//+T9n9eCOD3/4wznwwAPz/ve/P0cffXSS5Oc///njbrdp06b83//3/51169Zl0aJF+cM//MM873nPS6vVyvLly/P//D//Tz784Q8/7n4AAHrFdwACANBTp5xySpJHP+77WFse/rFl+Y684AUvyB/8wR/k137t17L33nsnefROvoMOOih//Md/nOc973l55JFHpve5Rbfbzcc//vEkyQtf+MK8613vyr777pskGR0dzVve8pa87GUvm9VTiNvtdv7iL/5ieviXJE972tMed7t/+Zd/yc9+9rMMDQ3loosuyq//+q+n1WolSZ797Gfn4osvziOPPPK4+wEA6BUDQAAAeurXfu3XsmTJkqxevTq33nprkmRqaio33nhjhoaGtvux3dlqtVrTDxDZsv8tfvrTn+bOO+9Mkrzuda+bcfvH3pW4Iy996Uuz33777XTf0qVLkyTHHXdcDj/88G2WL1myJCeddNJO7xcAYFf5CDAAAD13yimn5KMf/WhuuOGGHH300flf/+t/ZcOGDXn+85+fJz3pSbPax3333Zerrroq//qv/5o1a9ZkcnIymzdv3mqdtWvXbvX6Jz/5SZJk4cKFOeKII2bc79Oe9rTsv//+uffee3d4/KOOOmpWnb9sxYoVSR692297nv3sZ+/wI9AAAL3kDkAAAHruJS95SYaGhnLjjTdu9VHd2Xz8N0mWL1+e17/+9bniiity2223ZcOGDRkeHs7Y2FjGxsamv8Nv48aNW223fv36JMmTn/zkHe7/8ZYnyROf+MRZtf6yBx544HGPMZvjAwD0ijsAAQDouUWLFuU5z3lOvv3tb+fzn/98vvOd72ThwoV5wQte8Ljbbtq0KRdddFEmJyfzK7/yK3nrW9+ao446KgsWLJhe57rrrstf/uVfptvtztl72Jmn/gIA7MncAQgAwJzY8l1/H/3oR7N58+acdNJJ0w/02JEf/OAHue+++5IkF110UZ773OduNfxLkvHx8Rm33XLX3v3337/DY/zyR4d7actDR3bUMJfHBwD4ZQaAAADMid/8zd/MyMhINm3alGT2H//dMvwbHR3NgQceOOM6y5cvn/H3v/qrv5okeeihh7Jy5coZ11mzZs3jfv/f7jjyyCOTJLfccst219lePwDAXDAABABgTixYsCBvfetbc9ZZZ+X3fu/3cswxx8xquy3f7/fggw9Of5/eY333u9/Nd77znRm3Pfzww7N48eIkyeWXXz7jOpdddtmsOnbVC1/4wiTJN7/5zfzsZz/bZvmdd945/aRgAIB+MAAEAGDO/NZv/Vbe/OY3541vfOOstznmmGOyYMGCdLvdXHjhhbnrrruSJI888ki+/OUv573vfW/22Wef7W7/H//jf0yS3Hjjjfnv//2/Tz8Y5MEHH8xHPvKRfPGLX5weMs6FF73oRTn00EPT6XTyx3/8x/n2t789/V2Ft9xyS/7oj/4oe+2115wdHwDgl3kICAAAe5QnPOEJOe+88/KhD30o3/3ud/Pa1742CxcuzNTUVDZt2pTDDz88v/Vbv5VLLrlkxu1f/OIX50c/+lGuvPLKfP7zn88XvvCFLFy4MBs2bMjmzZtz1lln5Sc/+ckOP6K7O+bNm5c//dM/zbve9a7ce++9+cM//MPp7zDcuHFjxsbG8uY3vzl/+Zd/OSfHBwD4Ze4ABABgj/PqV786F154YY4++ugsWLAgnU4nBx10UN7whjfkQx/6UEZGRna4/X/+z/85f/qnf5qjjz468+fPT6fTyTOe8Yz8yZ/8Sd785jfPef+SJUvy0Y9+NGeccUYWLVqUTqeT0dHRnHbaafmbv/mbPO1pT5vzBgCALVrdLZ9HoNFWrFhROgEAAACA3bTlgWM7wx2AAAAAANBgBoAAAAAA0GAGgAAAAADQYJ4CDABAkuTWW2/NBRdcsFPbvPWtb82LX/ziOSoCAKAXDAABAEiSbNq0KePj4zu1zSOPPDJHNQAA9IqnAA8ITwEGAAAAqJ+nAAMAAAAAWzEABAAAAIAGMwAEAAAAgAYzAAQAAACABjMABAAAAIAGMwAEAAAAgAYzAAQAAACABmt1u91u6Qjm3sMPP5yRkZHSGQAAAAD02bzSAfTH5ORkpqam+nKs0dHRtNvtdDqdTExM9OWYvVRzv/Zyau7XXk7N/drLqblfezk192svp+Z+7eXU3K+9jJrbkzL9Y2NjO72NAeCA6Ha76XQ6fT9uiWP2Us392supuV97OTX3ay+n5n7t5dTcr72cmvu1l1Nzv/Yyam5P9ux+3wEIAAAAAA1mAAgAAAAADWYACAAAAAANZgAIAAAAAA1mAAgAAAAADWYACAAAAAANZgAIAAAAAA1mAAgAAAAADTavdEBN1q9fnyuvvDI333xz7r///syfPz+HHXZYXv7yl+e4447b6f11Op3ceuutWblyZVauXJmf/vSnufvuu5MkZ599dl772tf2+i0AAAAAMGAMAGdp1apVOf/887N+/fokyfDwcDZs2JDly5dn+fLlOf300/OmN71pp/a5du3avPe9752LXAAAAABIYgA4K7/4xS/y/ve/P+vXr8/BBx+cd7/73TnkkEMyNTWVq6++OpdffnmuueaaHHLIITn55JN3at/Dw8M59NBDc/jhh+ewww7LZz7zmdx1111z9E4AAAAAGDQGgLNwww035O677878+fNzwQUXZNGiRUmS+fPn56yzzsq6dety/fXX57LLLstJJ52UefNmd1oXLVqUK664Iq1Wa/p3n/vc5+bkPQAAAAAwmAwAZ+HGG29Mkpx44onTw7/HevWrX50vfvGLWbduXb7//e/n2GOPndV+h4Y8gwXYeQ/8l7f2dn893du/2/cvPzRHe2bQ1HLNJ657AAD2TAaAj2NycjK33XZbkuQ5z3nOjOssWrQoixcvzurVq3PLLbfMegC4p6j5f1j1uj3p7zCklnPftP9BW/t1U7NarvnEf988ngd6urd/55qfxT57vsdHuW72HK6bX9pfT/f272puT/x76vE80NO9/bumtQODwwDwcdx5553pdrtJkoMPPni76x188MFZvXp1Vq9e3a80KKqWP+gk/rADMIj8ewrg8Rkc/9L+erq3f+e/59kT+Azq41i3bt30z/vtt99219uybHx8fM6bAAAAAGC23AH4ODZu3Dj98/z587e73pZlk5OTc94EAAAAUEItd14m7hx9LHcAAgAAAECDuQPwcSxYsGD656mpqYyMjMy43tTUVJJkeHi4L12/7LLLLsunP/3p7S5/zWtek9e//vUzLntgjprmwtjY2FavHyiTsUt+uT2pp7/m9sR1U0rN7YnrppSa2xPXTSk1tyeum1Jqbk9cN6VoL6fm/pnae21oaGj673N9vAfmdO+95br5dwaAj+Ox3/u3bt267Q4At3xXYD/+gz2TDRs25N57793u8ocffjjtdruPRXOj5vegvZya+7WXU3O/9nJq7tdeTs392supuV97GTW3J3X397O91WpVfa56reZz0et2A8DHsXjx4rRarXS73axatSqLFy+ecb1Vq1YlSQ466KB+5k1buHBh9t9//+0uHxkZSafT6WPR3Kj5PWgvp+Z+7eXU3K+9nJr7tZdTc7/2cmru115Gze1J3f39aB8aGpqeXWzevHnOj1eLpl43uzIcNAB8HMPDwzniiCOyYsWKfOc738kJJ5ywzTpr167N6tWrkyTPetaz+p2YJDnnnHNyzjnnbHf52rVrG/GE4prfg/Zyau7XXk7N/drLqblfezk192svp+Z+7WXU3J7U3d+P9rGxsbTb7WzevLnqc9VrNZ+LHbU/+clP3un9eQjILJx00klJkptuuin33XffNsuvuuqqdLvd7LfffjnmmGP6XAcAAAAA22cAOAunnHJKDjjggGzcuDHve9/7cvvttyd59MEfV155Za677rokj96FN2/e1jdVnnfeeTnjjDPywQ9+cMZ9b9iwIRMTE9N/bblVd2pqaqvfb3nICAAAAADsDB8BnoW99tor73nPe3L++efnjjvuyDve8Y6MjIxk48aN0wO70047LSeffPJO7/vP//zPc+utt27z+8997nP53Oc+N/367LPPzmtf+9pdfxMAAAAADCQDwFlasmRJLrnkknz2s5/NzTffnLVr12bhwoU59NBDc+qpp+a4444rnQgAAAAA2zAA3An77rtvzj333Jx77rmz3uZjH/vYDpdfdNFFu5s1K61WK0ND9X/i2yO8y6i5Pam7X3s5NfdrL6fmfu3l1NyvvZya+7WXUXN7Und/v9trPle9VvO56HW7AeCAGB4ezsjIyIzL7u9zy+4YGxvb6nXN7Uk9/TW3J66bUmpuT1w3pdTcnrhuSqm5PXHdlFJze+K6KUV7OTX3z9Q+V9rt9pwfr5bznrhuHssAcEBMTk424kEiTX2E956u5vak7n7t5dTcr72cmvu1l1Nzv/Zyau7XXkbN7Und/f1oHx0dTbvdTqfTycTExJwfrxZNvW52ZThoADggut1uOp1O6YzdVvN70F5Ozf3ay6m5X3s5NfdrL6fmfu3l1NyvvYya25O6+/vdXvO56rWaz0Wv2+v/UjgAAAAAYLsMAAEAAACgwQwAAQAAAKDBDAABAAAAoMEMAAEAAACgwQwAAQAAAKDB5pUOoD9arVaGhuqf97bb7dIJu0x7OTX3ay+n5n7t5dTcr72cmvu1l1Nzv/Yyam5P6u7vd3vN56rXaj4XvW43ABwQw8PDGRkZmXHZ/X1u2R1jY2Nbva65Pamnv+b2xHVTSs3tieumlJrbE9dNKTW3J66bUmpuT1w3pWgvp+b+mdrnSrvdnvPj1XLeE9fNYxkADojJyclMTU2Vztht4+PjpRN2mfZyau7XXk7N/drLqblfezk192svp+Z+7WXU3J7U3d+P9tHR0bTb7XQ6nUxMTMz58WrR1OtmV4aDBoADotvtptPplM7YbTW/B+3l1NyvvZya+7WXU3O/9nJq7tdeTs392suouT2pu7/f7TWfq16r+Vz0ur3+L4UDAAAAALbLABAAAAAAGswAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAabF7pAPqj1WplaKj+eW+73S6dsMu0l1Nzv/Zyau7XXk7N/drLqblfezk192svo+b2pO7+frfXfK56reZz0et2A8ABMTw8nJGRkRmX3d/nlt0xNja21eua25N6+mtuT1w3pdTcnrhuSqm5PXHdlFJze+K6KaXm9sR1U4r2cmrun6l9rrTb7Tk/Xi3nPXHdPJYB4ICYnJzM1NRU6YzdNj4+Xjphl2kvp+Z+7eXU3K+9nJr7tZdTc7/2cmru115Gze1J3f39aB8dHU273U6n08nExMScH68WTb1udmU4aAA4ILrdbjqdTumM3Vbze9BeTs392supuV97OTX3ay+n5n7t5dTcr72MmtuTuvv73V7zueq1ms9Fr9vr/1I4AAAAAGC7DAABAAAAoMEMAAEAAACgwQwAAQAAAKDBDAABAAAAoMEMAAEAAACgwQwAAQAAAKDB5pUOoD9arVaGhuqf97bb7dIJu0x7OTX3ay+n5n7t5dTcr72cmvu1l1Nzv/Yyam5P6u7vd3vN56rXaj4XvW43ABwQw8PDGRkZmXHZ/X1u2R1jY2Nbva65Pamnv+b2xHVTSs3tieumlJrbE9dNKTW3J66bUmpuT1w3pWgvp+b+mdrnSrvdnvPj1XLeE9fNYxkADojJyclMTU2Vztht4+PjpRN2mfZyau7XXk7N/drLqblfezk192svp+Z+7WXU3J7U3d+P9tHR0bTb7XQ6nUxMTMz58WrR1OtmV4aDBoADotvtptPplM7YbTW/B+3l1NyvvZya+7WXU3O/9nJq7tdeTs392suouT2pu7/f7TWfq16r+Vz0ur3+L4UDAAAAALbLABAAAAAAGswAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAabF7pAPqj1WplaKj+eW+73S6dsMu0l1Nzv/Zyau7XXk7N/drLqblfezk192svo+b2pO7+frfXfK56reZz0et2A8ABMTw8nJGRkRmX3d/nlt0xNja21eua25N6+mtuT1w3pdTcnrhuSqm5PXHdlFJze+K6KaXm9sR1U4r2cmrun6l9rrTb7Tk/Xi3nPXHdPJYB4ICYnJzM1NRU6YzdNj4+Xjphl2kvp+Z+7eXU3K+9nJr7tZdTc7/2cmru115Gze1J3f39aB8dHU273U6n08nExMScH68WTb1udmU4aAA4ILrdbjqdTumM3Vbze9BeTs392supuV97OTX3ay+n5n7t5dTcr72MmtuTuvv73V7zueq1ms9Fr9vr/1I4AAAAAGC7DAABAAAAoMEMAAEAAACgwQwAAQAAAKDBDAABAAAAoMEMAAEAAACgwQwAAQAAAKDBDAABAAAAoMEMAAEAAACgwQwAAQAAAKDBDAABAAAAoMHmlQ6gP1qtVoaG6p/3ttvt0gm7THs5NfdrL6fmfu3l1NyvvZya+7WXU3O/9jJqbk/q7u93e83nqtdqPhe9bjcAHBDDw8MZGRmZcdn9fW7ZHWNjY1u9rrk9qae/5vbEdVNKze2J66aUmtsT100pNbcnrptSam5PXDelaC+n5v6Z2udKu92e8+PVct4T181jGQAOiMnJyUxNTZXO2G3j4+OlE3aZ9nJq7tdeTs392supuV97OTX3ay+n5n7tZdTcntTd34/20dHRtNvtdDqdTExMzPnxatHU62ZXhoMGgAOi2+2m0+mUzthtNb8H7eXU3K+9nJr7tZdTc7/2cmru115Ozf3ay6i5Pam7v9/tNZ+rXqv5XPS6vf4vhQMAAAAAtssAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAazAAQAAAAABpsXukA+qPVamVoqP55b7vdLp2wy7SXU3O/9nJq7tdeTs392supuV97OTX3ay+j5vak7v5+t9d8rnqt5nPR63YDwAExPDyckZGRGZfd3+eW3TE2NrbV65rbk3r6a25PXDel1NyeuG5Kqbk9cd2UUnN74roppeb2xHVTivZyau6fqX2utNvtOT9eLec9cd08lgHggJicnMzU1FTpjN02Pj5eOmGXaS+n5n7t5dTcr72cmvu1l1Nzv/Zyau7XXkbN7Und/f1oHx0dTbvdTqfTycTExJwfrxZNvW52ZThoADggut1uOp1O6YzdVvN70F5Ozf3ay6m5X3s5NfdrL6fmfu3l1NyvvYya25O6+/vdXvO56rWaz0Wv2+v/UjgAAAAAYLsMAAEAAACgwQwAAQAAAKDBDAABAAAAoMEMAAEAAACgwQwAAQAAAKDBDAABAAAAoMEMAAEAAACgwQwAAQAAAKDBDAABAAAAoMEMAAEAAACgwQwAAQAAAKDBDAABAAAAoMEMAAEAAACgwQwAAQAAAKDBDAABAAAAoMHmlQ6gP1qtVoaG6p/3ttvt0gm7THs5NfdrL6fmfu3l1NyvvZya+7WXU3O/9jJqbk/q7u93e83nqtdqPhe9bjcAHBDDw8MZGRmZcdn9fW7ZHWNjY1u9rrk9qae/5vbEdVNKze2J66aUmtsT100pNbcnrptSam5PXDelaC+n5v6Z2udKu92e8+PVct4T181jGQAOiMnJyUxNTZXO2G3j4+OlE3aZ9nJq7tdeTs392supuV97OTX3ay+n5n7tZdTcntTd34/20dHRtNvtdDqdTExMzPnxatHU62ZXhoMGgAOi2+2m0+mUzthtNb8H7eXU3K+9nJr7tZdTc7/2cmru115Ozf3ay6i5Pam7v9/tNZ+rXqv5XPS6vf4vhQMAAAAAtssAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAabF7pgH5av359rrzyytx88825//77M3/+/Bx22GF5+ctfnuOOO26X97tp06Zce+21Wbp0adasWZMkedrTnpYXvvCFOfXUUzNv3synedmyZfnBD36Q2267LWvXrs369evT7XYzNjaWpz/96XnZy16Wo446ape7AAAAAGBgBoCrVq3K+eefn/Xr1ydJhoeHs2HDhixfvjzLly/P6aefnje96U07vd/Jycm8973vzYoVK5Ike++9d5Jk5cqVWblyZb7+9a/nwgsvzIIFC7bZ9pOf/GR+/vOfT79euHBhpqamcs899+See+7J0qVL84pXvCLnnnvurrxlAAAAABiMAeAvfvGLvP/978/69etz8MEH593vfncOOeSQTE1N5eqrr87ll1+ea665JoccckhOPvnkndr3pZdemhUrVmThwoV5+9vfPn0n4bJly/LXf/3X+fGPf5wPf/jDede73rXNti94wQuy//775xnPeEae8pSnZK+99kq3283Pf/7z/OM//mP+5V/+JVdffXUOO+ywnHTSSb04FQAAAAAMmIEYAN5www25++67M3/+/FxwwQVZtGhRkmT+/Pk566yzsm7dulx//fW57LLLctJJJ233I7u/7Pbbb89NN92UJHnb296W448/fnrZ8ccfn82bN+cDH/hAbrzxxrzqVa/KwQcfvNX2r3vd67bZZ6vVyuLFi/POd74z99xzT374wx/mK1/5igEgAAAANMQD/+Wtvd1fT/f27/b9yw/N0Z7pt4F4CMiNN96YJDnxxBOnh3+P9epXvzqtVivr1q3L97///Vnvd+nSpel2uznwwAO3Gv5tccIJJ+TAAw9Mt9vN0qVLd6q51WrliCOOSJLcf//9O7UtAAAAAGzR+AHg5ORkbrvttiTJc57znBnXWbRoURYvXpwkueWWW2a97+9973tJkmOPPTatVmub5a1WK8cee+xW687W5s2b85Of/CRJcsABB+zUtgAAAACwReM/AnznnXem2+0myTYfwX2sgw8+OKtXr87q1atntd9ut5s777zzcfe7ZMmSJJn1fh966KHceeed+fznP58f//jHSZLTTjttVtsCAAAAwC9r/ABw3bp10z/vt99+211vy7Lx8fFZ7XdycjIbN26c9X4nJyczOTmZ4eHhbdZZtmxZLrroom1+v3Dhwpx33nnbvXMRAAAAAB5P4z8CvGVIlzz60I/t2bJscnJyVvt97Hqz2e+O9r3XXntl3333zROf+MQMDT36j2R4eDivf/3r84IXvGBWPQAAAAAwk8bfAViD5z73ufn7v//7JMmmTZtyxx135NOf/nQuvfTSfOlLX8oFF1yww7sMAQAAAGB7Gj8AXLBgwfTPU1NTGRkZmXG9qampJJnxI7ozeex6W7bd0X5nu+958+bl8MMPzwUXXJCLLrooy5Yty0c+8pH8yZ/8yQ63u+yyy/LpT396u8tf85rX5PWvf/2Myx543Ko9x9jY2FavHyiTsUt+uT2pp7/m9sR1U0rN7YnrppSa2xPXTSk1tyeum1Jqbk9cN6VoL6fmfu3l1Nw/U/vuaPwA8LF3zq1bt267A8At3xU42xM8PDyc4eHhTE5ObvU9g9vb75b1d8bpp5+eZcuWZdmyZZmYmMjo6Oh2192wYUPuvffe7S5/+OGH0263d+r4e6Ka34P2cmru115Ozf3ay6m5X3s5NfdrL6fmfu1l1Nye1N2vvZya+3vd3vgB4OLFi9NqtdLtdrNq1aosXrx4xvVWrVqVJDnooINmtd9Wq5XFixfntttum962F/t9rCc96UnTP9999907HAAuXLgw+++//3aXj4yMpNPp7HTDnqbm96C9nJr7tZdTc7/2cmru115Ozf3ay6m5X3sZNbcndfdrL6fm/h2178pwsPEDwOHh4RxxxBFZsWJFvvOd7+SEE07YZp21a9dm9erVSZJnPetZs973M5/5zNx222357ne/u911li9fPr3uzrr77runf37sR5lncs455+Scc87Z7vK1a9fO+gnHe7Ka34P2cmru115Ozf3ay6m5X3s5NfdrL6fmfu1l1Nye1N2vvZya+3fU/uQnP3mn99f4pwAnyUknnZQkuemmm3Lfffdts/yqq65Kt9vNfvvtl2OOOWbW+z3xxBPTarWyZs2afPOb39xm+Te+8Y2sWbMmrVZrumGLx5tCdzqdfP7zn0+SPPGJT9zunYsAAAAAsCMDMQA85ZRTcsABB2Tjxo153/vel9tvvz3Jow/ouPLKK3PdddclefQuunnztr4p8rzzzssZZ5yRD37wg9vs95BDDsmJJ56YJLnkkkuybNmydLvddLvdLFu2LB/60IeSPDqAXLJkyVbb3njjjfnzP//zfOtb38qDDz44/ftNmzblBz/4Qf7sz/5s+u7B3/md38nQ0ED8owIAAACgxxr/EeAk2WuvvfKe97wn559/fu6444684x3vyMjISDZu3JjNmzcnSU477bScfPLJO73vt7zlLbnrrruyYsWKXHTRRdl7772TJI888kiS5OlPf3re/OY3z7jtt771rXzrW99K8uhHlefNm5eHH354+u7AoaGh/PZv/3ZOO+20ne4CAAAAgGRABoBJsmTJklxyySX57Gc/m5tvvjlr167NwoULc+ihh+bUU0/Ncccdt0v7HR4ezsUXX5xrr702S5cuzZo1a5Ikhx12WE466aSceuqp29xVmCTPe97z8pa3vCXf//73c8cdd+SBBx7Iww8/nAULFuSAAw7IUUcdlZe+9KXb3DkIAAAAADtjYAaASbLvvvvm3HPPzbnnnjvrbT72sY897jrz5s3LmWeemTPPPHPW+33iE5+Yl73sZXnZy1426212R6vVasTHiD3Cu4ya25O6+7WXU3O/9nJq7tdeTs392supuV97GTW3J3X3ay+n5v5etw/UAHCQDQ8PZ2RkZMZl9/e5ZXeMjY1t9brm9qSe/prbE9dNKTW3J66bUmpuT1w3pdTcnrhuSqm5PXHdlKK9nJr7tZdTc/9M7bvDAHBATE5OZmpqqnTGbmvqI7z3dDW3J3X3ay+n5n7t5dTcr72cmvu1l1Nzv/Yyam5P6u7XXk7N/Ttq35XhoAHggOh2u9MPF6lZze9Bezk192svp+Z+7eXU3K+9nJr7tZdTc7/2MmpuT+ru115Ozf29bq//S+EAAAAAgO0yAAQAAACABjMABAAAAIAGMwAEAAAAgAYzAAQAAACABvMU4AHRarUyNFT/vLfdbpdO2GXay6m5X3s5NfdrL6fmfu3l1NyvvZya+7WXUXN7Une/9nJq7u91uwHggBgeHs7IyMiMy+7vc8vuGBsb2+p1ze1JPf01tyeum1Jqbk9cN6XU3J64bkqpuT1x3ZRSc3viuilFezk192svp+b+mdp3hwHggJicnMzU1FTpjN02Pj5eOmGXaS+n5n7t5dTcr72cmvu1l1Nzv/Zyau7XXkbN7Und/drLqbl/R+27Mhw0ABwQ3W43nU6ndMZuq/k9aC+n5n7t5dTcr72cmvu1l1Nzv/Zyau7XXkbN7Und/drLqbm/1+31fykcAAAAALBdBoAAAAAA0GAGgAAAAADQYAaAAAAAANBgBoAAAAAA0GAGgAAAAADQYPNKB9AfrVYrQ0P1z3vb7XbphF2mvZya+7WXU3O/9nJq7tdeTs392supuV97GTW3J3X3ay+n5v5etxsADojh4eGMjIzMuOz+PrfsjrGxsa1e19ye1NNfc3viuiml5vbEdVNKze2J66aUmtsT100pNbcnrptStJdTc7/2cmrun6l9dxgADojJyclMTU2Vztht4+PjpRN2mfZyau7XXk7N/drLqblfezk192svp+Z+7WXU3J7U3a+9nJr7d9S+K8NBA8AB0e120+l0Smfstprfg/Zyau7XXk7N/drLqblfezk192svp+Z+7WXU3J7U3a+9nJr7e91e/5fCAQAAAADbZQAIAAAAAA1mAAgAAAAADWYACAAAAAANZgAIAAAAAA1mAAgAAAAADTavdAD90Wq1MjRU/7y33W6XTthl2supuV97OTX3ay+n5n7t5dTcr72cmvu1l1Fze1J3v/Zyau7vdbsB4IAYHh7OyMjIjMvu73PL7hgbG9vqdc3tST39NbcnrptSam5PXDel1NyeuG5Kqbk9cd2UUnN74ropRXs5NfdrL6fm/pnad4cB4ICYnJzM1NRU6YzdNj4+Xjphl2kvp+Z+7eXU3K+9nJr7tZdTc7/2cmru115Gze1J3f3ay6m5f0ftuzIcNAAcEN1uN51Op3TGbqv5PWgvp+Z+7eXU3K+9nJr7tZdTc7/2cmru115Gze1J3f3ay6m5v9ft9X8pHAAAAACwXQaAAAAAANBgBoAAAAAA0GAGgAAAAADQYAaAAAAAANBgBoAAAAAA0GAGgAAAAADQYAaAAAAAANBg80oH0B+tVitDQ/XPe9vtdumEXaa9nJr7tZdTc7/2cmru115Ozf3ay6m5X3sZNbcndfdrL6fm/l63GwAOiOHh4YyMjMy47P4+t+yOsbGxrV7X3J7U019ze+K6KaXm9sR1U0rN7YnrppSa2xPXTSk1tyeum1K0l1Nzv/Zyau6fqX13GAAOiMnJyUxNTZXO2G3j4+OlE3aZ9nJq7tdeTs392supuV97OTX3ay+n5n7tZdTcntTdr72cmvt31L4rw0EDwAHR7XbT6XRKZ+y2mt+D9nJq7tdeTs392supuV97OTX3ay+n5n7tZdTcntTdr72cmvt73V7/l8IBAAAAANtlAAgAAAAADWYACAAAAAANZgAIAAAAAA1mAAgAAAAADWYACAAAAAANZgAIAAAAAA1mAAgAAAAADWYACAAAAAANZgAIAAAAAA02r3QA/dFqtTI0VP+8t91ul07YZdrLqblfezk192svp+Z+7eXU3K+9nJr7tZdRc3tSd7/2cmru73W7AeCAGB4ezsjIyIzL7u9zy+4YGxvb6nXN7Uk9/TW3J66bUmpuT1w3pdTcnrhuSqm5PXHdlFJze+K6KUV7OTX3ay+n5v6Z2neHAeCAmJyczNTUVOmM3TY+Pl46YZdpL6fmfu3l1NyvvZya+7WXU3O/9nJq7tdeRs3tSd392supuX9H7bsyHDQAHBDdbjedTqd0xm6r+T1oL6fmfu3l1NyvvZya+7WXU3O/9nJq7tdeRs3tSd392supub/X7fV/KRwAAAAAsF0GgAAAAADQYAaAAAAAANBgBoAAAAAA0GAGgAAAAADQYAaAAAAAANBgBoAAAAAA0GAGgAAAAADQYAaAAAAAANBgBoAAAAAA0GAGgAAAAADQYPNKB9AfrVYrQ0P1z3vb7XbphF2mvZya+7WXU3O/9nJq7tdeTs392supuV97GTW3J3X3ay+n5v5etxsADojh4eGMjIzMuOz+PrfsjrGxsa1e19ye1NNfc3viuiml5vbEdVNKze2J66aUmtsT100pNbcnrptStJdTc7/2cmrun6l9dxgADojJyclMTU2Vztht4+PjpRN2mfZyau7XXk7N/drLqblfezk192svp+Z+7WXU3J7U3a+9nJr7d9S+K8NBA8AB0e120+l0Smfstprfg/Zyau7XXk7N/drLqblfezk192svp+Z+7WXU3J7U3a+9nJr7e91e/5fCAQAAAADbZQAIAAAAAA1mAAgAAAAADWYACAAAAAANZgAIAAAAAA1mAAgAAAAADWYACAAAAAANZgAIAAAAAA1mAAgAAAAADWYACAAAAAANZgAIAAAAAA1mAAgAAAAADWYACAAAAAANZgAIAAAAAA02r3QA/dFqtTI0VP+8t91ul07YZdrLqblfezk192svp+Z+7eXU3K+9nJr7tZdRc3tSd7/2cmru73W7AeCAGB4ezsjIyIzL7u9zy+4YGxvb6nXN7Uk9/TW3J66bUmpuT1w3pdTcnrhuSqm5PXHdlFJze+K6KUV7OTX3ay+n5v6Z2neHAeCAmJyczNTUVOmM3TY+Pl46YZdpL6fmfu3l1NyvvZya+7WXU3O/9nJq7tdeRs3tSd392supuX9H7bsyHDQAHBDdbjedTqd0xm6r+T1oL6fmfu3l1NyvvZya+7WXU3O/9nJq7tdeRs3tSd392supub/X7fV/KRwAAAAAsF0GgAAAAADQYAaAAAAAANBgBoAAAAAA0GAGgAAAAADQYAaAAAAAANBgBoAAAAAA0GDzSgf00/r163PllVfm5ptvzv3335/58+fnsMMOy8tf/vIcd9xxu7zfTZs25dprr83SpUuzZs2aJMnTnva0vPCFL8ypp56aefNmPs333HNPfvjDH2blypX56U9/mp/97GfZuHFjkuQLX/jCLvcAAAAAwBYDMwBctWpVzj///Kxfvz5JMjw8nA0bNmT58uVZvnx5Tj/99LzpTW/a6f1OTk7mve99b1asWJEk2XvvvZMkK1euzMqVK/P1r389F154YRYsWLDNtp/5zGfy1a9+dTfeFQAAAADs2EAMAH/xi1/k/e9/f9avX5+DDz447373u3PIIYdkamoqV199dS6//PJcc801OeSQQ3LyySfv1L4vvfTSrFixIgsXLszb3/726TsJly1blr/+67/Oj3/843z4wx/Ou971rm22bbVaOfDAA3P44YfnsMMOy+TkZP7hH/6hJ+8ZAAAAAJIBGQDecMMNufvuuzN//vxccMEFWbRoUZJk/vz5Oeuss7Ju3bpcf/31ueyyy3LSSSdt9yO7v+z222/PTTfdlCR529veluOPP3562fHHH5/NmzfnAx/4QG688ca86lWvysEHH7zV9m9961vTbrenXy9btmx33yoAAAAAbGUgHgJy4403JklOPPHE6eHfY7361a9Oq9XKunXr8v3vf3/W+126dGm63W4OPPDArYZ/W5xwwgk58MAD0+12s3Tp0m2WP3b4BwAAAABzofEDwMnJydx2221Jkuc85zkzrrNo0aIsXrw4SXLLLbfMet/f+973kiTHHntsWq3WNstbrVaOPfbYrdYFAAAAgH5q/ADwzjvvTLfbTZJtPoL7WFuWrV69elb77Xa7ufPOOx93v0uWLNmp/QIAAABALzV+ALhu3brpn/fbb7/trrdl2fj4+Kz2Ozk5mY0bN856v5OTk5mcnJzVvgEAAACgVxo/ANwypEsefejH9mxZNtsh3WPXm81+d2bfAAAAANArjR8AAgAAAMAgm1c6YK4tWLBg+uepqamMjIzMuN7U1FSSZHh4eFb7fex6W7bd0X53Zt+74rLLLsunP/3p7S5/zWtek9e//vUzLntgjprmwtjY2FavHyiTsUt+uT2pp7/m9sR1U0rN7YnrppSa2xPXTSk1tyeum1Jqbk9cN6VoL6fmfu3l1Nw/U/vuaPwA8LHfz7du3brtDgC3fFfgbE/w8PBwhoeHMzk5udX3DG5vv1vWnysbNmzIvffeu93lDz/8cNrt9pwdv19qfg/ay6m5X3s5NfdrL6fmfu3l1NyvvZya+7WXUXN7Une/9nJq7u91e+MHgIsXL06r1Uq3282qVauyePHiGddbtWpVkuSggw6a1X5brVYWL16c2267bXrbXux3Vy1cuDD777//dpePjIyk0+nMaUM/1PwetJdTc7/2cmru115Ozf3ay6m5X3s5NfdrL6Pm9qTufu3l1Ny/o/ZdGQ42fgA4PDycI444IitWrMh3vvOdnHDCCduss3bt2qxevTpJ8qxnPWvW+37mM5+Z2267Ld/97ne3u87y5cun151L55xzTs4555ztLl+7du2sn3C8J6v5PWgvp+Z+7eXU3K+9nJr7tZdTc7/2cmru115Gze1J3f3ay6m5f0ftT37yk3d6fwPxEJCTTjopSXLTTTflvvvu22b5VVddlW63m/322y/HHHPMrPd74oknptVqZc2aNfnmN7+5zfJvfOMbWbNmTVqt1nQDAAAAAPTTQAwATznllBxwwAHZuHFj3ve+9+X2229P8ugDOq688spcd911SR69i27evK1vijzvvPNyxhln5IMf/OA2+z3kkENy4oknJkkuueSSLFu2LN1uN91uN8uWLcuHPvShJI8OIJcsWbLN9ps2bcrExMT0X5OTk9PLHvv7iYmJnpwHAAAAAAZP4z8CnCR77bVX3vOe9+T888/PHXfckXe84x0ZGRnJxo0bs3nz5iTJaaedlpNPPnmn9/2Wt7wld911V1asWJGLLrooe++9d5LkkUceSZI8/elPz5vf/OYZt/3Rj36U888/f8Zlv/xx3i984Qs73QYAAAAAAzEATJIlS5bkkksuyWc/+9ncfPPNWbt2bRYuXJhDDz00p556ao477rhd2u/w8HAuvvjiXHvttVm6dGnWrFmTJDnssMNy0kkn5dRTT93mrkIAAAAA6JeBmkztu+++Offcc3PuuefOepuPfexjj7vOvHnzcuaZZ+bMM8/cqZ5jjjnGnX0AAAAAzKmBGgAOslarlaGh+r/ycVcedb2n0F5Ozf3ay6m5X3s5NfdrL6fmfu3l1NyvvYya25O6+7WXU3N/r9sNAAfE8PBwRkZGZlx2f59bdsfY2NhWr2tuT+rpr7k9cd2UUnN74roppeb2xHVTSs3tieumlJrbE9dNKdrLqblfezk198/UvjsMAAfE5ORkpqamSmfstvHx8dIJu0x7OTX3ay+n5n7t5dTcr72cmvu1l1Nzv/Yyam5P6u7XXk7N/Ttq35XhoAHggOh2u+l0OqUzdlvN70F7OTX3ay+n5n7t5dTcr72cmvu1l1Nzv/Yyam5P6u7XXk7N/b1ur/9L4QAAAACA7TIABAAAAIAGMwAEAAAAgAYzAAQAAACABjMABAAAAIAG8xTgAdFqtTI0VP+8t91ul07YZdrLqblfezk192svp+Z+7eXU3K+9nJr7tZdRc3tSd7/2cmru73W7AeCAGB4ezsjIyIzL7u9zy+4YGxvb6nXN7Uk9/TW3J66bUmpuT1w3pdTcnrhuSqm5PXHdlFJze+K6KUV7OTX3ay+n5v6Z2neHAeCAmJyczNTUVOmM3TY+Pl46YZdpL6fmfu3l1NyvvZya+7WXU3O/9nJq7tdeRs3tSd392supuX9H7bsyHDQAHBDdbjedTqd0xm6r+T1oL6fmfu3l1NyvvZya+7WXU3O/9nJq7tdeRs3tSd392supub/X7fV/KRwAAAAAsF0GgAAAAADQYAaAAAAAANBgBoAAAAAA0GAGgAAAAADQYAaAAAAAANBg80oH0B+tVitDQ/XPe9vtdumEXaa9nJr7tZdTc7/2cmru115Ozf3ay6m5X3sZNbcndfdrL6fm/l63GwAOiOHh4YyMjMy47P4+t+yOsbGxrV7X3J7U019ze+K6KaXm9sR1U0rN7YnrppSa2xPXTSk1tyeum1K0l1Nzv/Zyau6fqX13GAAOiMnJyUxNTZXO2G3j4+OlE3aZ9nJq7tdeTs392supuV97OTX3ay+n5n7tZdTcntTdr72cmvt31L4rw0EDwAHR7XbT6XRKZ+y2mt+D9nJq7tdeTs392supuV97OTX3ay+n5n7tZdTcntTdr72cmvt73V7/l8IBAAAAANtlAAgAAAAADWYACAAAAAANZgAIAAAAAA1mAAgAAAAADWYACAAAAAANZgAIAAAAAA1mAAgAAAAADTavdAD90Wq1MjRU/7y33W6XTthl2supuV97OTX3ay+n5n7t5dTcr72cmvu1l1Fze1J3v/Zyau7vdbsB4IAYHh7OyMjIjMvu73PL7hgbG9vqdc3tST39NbcnrptSam5PXDel1NyeuG5Kqbk9cd2UUnN74ropRXs5NfdrL6fm/pnad4cB4ICYnJzM1NRU6YzdNj4+Xjphl2kvp+Z+7eXU3K+9nJr7tZdTc7/2cmru115Gze1J3f3ay6m5f0ftuzIcNAAcEN1uN51Op3TGbqv5PWgvp+Z+7eXU3K+9nJr7tZdTc7/2cmru115Gze1J3f3ay6m5v9ft9X8pHAAAAACwXQaAAAAAANBgBoAAAAAA0GAGgAAAAADQYAaAAAAAANBgBoAAAAAA0GAGgAAAAADQYAaAAAAAANBgBoAAAAAA0GDzSgfQH61WK0ND9c972+126YRdpr2cmvu1l1Nzv/Zyau7XXk7N/drLqblfexk1tyd192svp+b+XrcbAA6I4eHhjIyMzLjs/j637I6xsbGtXtfcntTTX3N74roppeb2xHVTSs3tieumlJrbE9dNKTW3J66bUrSXU3O/9nJq7p+pfXcYAA6IycnJTE1Nlc7YbePj46UTdpn2cmru115Ozf3ay6m5X3s5NfdrL6fmfu1l1Nye1N2vvZya+3fUvivDQQPAAdHtdtPpdEpn7Laa34P2cmru115Ozf3ay6m5X3s5NfdrL6fmfu1l1Nye1N2vvZya+3vdXv+XwgEAAAAA22UACAAAAAANZgAIAAAAAA1mAAgAAAAADWYACAAAAAANZgAIAAAAAA1mAAgAAAAADWYACAAAAAANZgAIAAAAAA1mAAgAAAAADTavdAD90Wq1MjRU/7y33W6XTthl2supuV97OTX3ay+n5n7t5dTcr72cmvu1l1Fze1J3v/Zyau7vdbsB4IAYHh7OyMjIjMvu73PL7hgbG9vqdc3tST39NbcnrptSam5PXDel1NyeuG5Kqbk9cd2UUnN74ropRXs5NfdrL6fm/pnad4cB4ICYnJzM1NRU6YzdNj4+Xjphl2kvp+Z+7eXU3K+9nJr7tZdTc7/2cmru115Gze1J3f3ay6m5f0ftuzIcNAAcEN1uN51Op3TGbqv5PWgvp+Z+7eXU3K+9nJr7tZdTc7/2cmru115Gze1J3f3ay6m5v9ft9X8pHAAAAACwXQaAAAAAANBgBoAAAAAA0GB9+Q7ATZs25X/9r/+Vm2++OWvWrMmDDz6YffbZJ0996lPzG7/xG3nBC15Q9aOZAQAAAGBPNacDwM2bN+cv//Iv88EPfjD33HPPdtc74IAD8q53vSvvfve7MzTkpkQAAAAA6JVWt9vtzsWOH3jggbz85S/Pt771rSSPPoV2hyGtVo477rhcd9112XfffeciCQAAAAAGzpwMALvdbn7zN38z3/jGN5IkQ0NDeclLXpKTTz45RxxxRBYuXJgNGzZk5cqV+ed//ud8+ctfTqfTSavVygknnJCvfe1rvU4CAAAAgIE0JwPAv/u7v8t5552XVquVww8/PP/wD/+QZz/72dtd/5ZbbsnZZ5+dn/zkJ2m1Wvnbv/3bvOENb+h1FgAAAAAMnDkZAL74xS/OjTfemH333Te33nprnvrUpz7uNmvWrMnRRx+d9evX54UvfGG++tWv9joLAAAAAAbOnDxx4/vf/35arVbe+MY3zmr4lyRPfepTc+6556bb7eb73//+XGQBAAAAwMCZkwHghg0bkiTPfe5zd2q75zznOUmShx9+uOdNAAAAADCI5mQAuOWuv06ns1PbbVn/wAMP7HkTAAAAAAyiORkAnnjiiUky/RTg2frGN76RVquVF77whXORBQAAAAADZ04eAvKv//qvOe6447LXXnvlX//1X/OMZzzjcbf50Y9+lOc+97nZtGlTli1bNv1xYAAAAABg183JHYDPfe5z8xd/8ReZmprKi1/84lx//fU7XP+LX/xi/sN/+A955JFH8l//6381/AMAAACAHtmtOwD//u//fofLv/CFL+Sqq65Kq9XK05/+9Jx88sk54ogjsnDhwmzYsCErV67Ml7/85fz4xz9OkrzqVa/K6aefniT5/d///V3NAgAAAAD+t90aAA4NDaXVaj3uet1ud4fr/fLyVquVTZs27WoWAAAAAPC/zdvdHcx2fvh4683BVxECAAAAwMDbrQHgxz/+8V51AAAAAABzYE6eAsyeZ8WKFaUTAAAAANhNRx555E5vMydPAQYAAAAA9gwGgAAAAADQYLv9EJCdcc899+Suu+7Kgw8+mH322SdPfepTs//++/czAQAAAAAGypwPAFetWpX//t//e6666qqsWrVqm+VLlizJa17zmrz97W/PQQcdNNc5AAAAADBQ5vQhIB//+Mfz9re/PQ8//HCSZKZDtVqtJMnIyEguueSSvOENb5irnIHmISAAAAAA9duVh4DM2R2AH//4x3Puueem1Wql2+2m1WrlGc94Ro488sg84QlPyEMPPZQVK1bkxz/+cbrdbjZs2JBzzz03SQwBAQAAAKBH5uQOwLvuuitHHHFEHn744bRarfzBH/xB/q//6//KkiVLtll39erVufjii/M3f/M32bx5cxYuXJjbbrstBxxwQK+zBpo7AAEAAADqt8fcAXjppZdOD/8++tGP5o1vfON21z3ooIPyP/7H/8iv//qv541vfGMefvjhXHrppbnwwgvnIg0AgD76xCc+kU9+8pPb/H7vvffO2NhYjjzyyLz0pS/NC17wggJ1AACDYWgudnrDDTek1WrlpS996Q6Hf4/1hje8IS972cvS7XbzpS99aS6yAAAoZGhoKGNjY9N/dbvd3HPPPfna176W9773vXn/+9+fzZs3l84EAGikObkD8Gc/+1mS5Mwzz9yp7V7xilfkS1/60vT2AAA0w6JFi3LFFVdMv+52u1m9enX+3//3/83Xv/71fOUrX8kzn/nMnHHGGQUrAQCaaU7uAHzwwQeTJPvtt99Obbdl/YceeqjnTQAA7DlarVaWLFmSP/3TP53+nugbbrihcBUAQDPNyR2AT3rSk3LPPffk9ttv36nt7rjjjiQ7PzgEAKBOe+21V4499tisWrVq+s+CM1m2bFmuueaa/OhHP8qDDz6YffbZJ894xjPyile8Is9//vNn3Oad73xnbrnllrz+9a/POeeck//5P/9nvvzlL+euu+7KPvvsk+c///l54xvfmCc96UlJkp///Oe57LLL8q//+q954IEHcsABB+TUU0/Nb//2b2doaPv/v/lPfvKTXHXVVbnllluybt267L333lmyZEle/OIX54wzzsjee++9W+cIAGB3zckA8Oijj87dd9+dT33qU/kv/+W/7PAPTFt0Op186lOfSqvVytFHHz0XWQAA7IG63W6SzPgdgN1uN3/1V3+Va665Jsmj3yU4MjKS9evX5xvf+Ea+8Y1v5Mwzz8zb3/72tFqtGfe/adOm/OEf/mGWL18+PYxbu3Ztrr/++nzve9/Lhz70ofz85z/PH/3RH+Whhx7KwoULs2nTpqxevTof+chHct999+Wtb33rjPv+5Cc/mU9+8pPT72FkZCSTk5P50Y9+lB/96Ef5p3/6p/zFX/xFnvjEJ+72eQIA2FVz8hHgLd/d8sMf/jBvectbpv9AtD3dbjf/+T//59x6661JHv0uQAAAmu8Xv/hFvvvd7yZJnvrUp26z/B//8R+nh3+/+7u/m89//vO55ppr8rnPfS5nnXVWkuTzn/98rrrqqu0e4+qrr86qVaty0UUX5Ytf/GKuv/76vO9978vIyEjuvPPOfPzjH8+FF16YY445JpdffnmuvfbaXHPNNdN/pr3qqqvyb//2bzPu9xOf+ET22WefvP3tb8/VV1+d6667Ll/60pfygQ98IIsXL85tt92Wiy++eLfPEwDA7piTAeB5552XxYsXJ0k++tGP5jnPeU4uv/zy3HvvvVutd9999+Xyyy/Pc5/73Hz0ox9Nq9XK4sWLc955581FFgAAe4hut5tVq1blwgsvzOrVq5MkL33pS7daZ+PGjfnUpz6VJHnlK1+Z//Sf/lP22WefJMno6Gje/OY357TTTkuS/P3f/32mpqZmPNZDDz2U9773vTn++OMzNDSUdrudF7zgBfmd3/mdJI8O8vbee++8733vmx5CLly4MO94xzvytKc9Ld1uN0uXLt1qnxs2bMhHP/rRzJs3LxdffHFe+cpXZnR0NMmjH2t+/vOfn4svvjgLFizIsmXLsmLFil6cNgCAXTInHwFesGBBPvvZz+bFL35xHn744Xzve9/L7//+7ydJ9tlnnyxcuDAbNmyYflhI8ugfAhcuXJirrroq8+fPn4ssAAAKue+++/KqV71q+vVDDz2UX/ziF9Ovjz/++Lzyla/captvf/vbeeihhzI0NJRzzjlnxv3+/u//fq6//vpMTEzk29/+dv6P/+P/2Gado446Ks9+9rO3+f1zn/vcfPzjH0+SnHXWWWm321stHxoayrOf/ez8/Oc/z89+9rOtli1dujQbNmzIc57znDzjGc+Yse1pT3tanvGMZ+S73/1uvv3tb+fII4+ccT0AgLk2JwPAJPn1X//1fP3rX8/rXve6/OAHP5j+/cTERB588MFtPhZ8zDHH5LLLLssxxxwzV0kAABSyefPmjI+Pz7jsP/2n/5Szzz57m+/w23LX3K/8yq9s9yFxixYtypIlS3LHHXdkxYoVMw4ADznkkBm33XfffR93nS3Hfeihh7b6/ZY/3/7whz/carD5yzZs2JAkueeee7a7DgDAXJuzAWCSPPOZz8z3vve9XHfddbnqqqvyrW99K3fdddf0k9sOPPDA/MZv/EZe/epX5+Uvf/l2v7gZAIC6PeUpT8kVV1yR5NGHv91333350pe+lE996lP5xCc+kSOPPDLPfe5zt9rmgQceSJI8+clP3uG+Fy1alDvuuGN6/V+25Sm/v+yxd/xtb50tD7PbtGnTVr+///77kzz6MeWNGzfusC/Jdj+eDADQD3M6AEySVquV0047bfr7WQAAGGztdjsHHHBA3vCGN2ThwoW59NJLc+GFF+bv/u7vtjuI29NseWLx6aefnne/+92FawAAdmxOHgJy6KGH5tBDD83b3/72udg9AAAN8epXvzqHHnpoJiYm8rd/+7dbLdvyEd21a9fucB/33XffVuv3w9jYWJJs85A7AIA90ZwMAFetWpV/+7d/y1FHHTUXuwcAoCGGhobye7/3e0mSG264IatWrZpetuWhGXfccUfWrVs34/Zr166d3qafD9nY8ufc733ve9Pf8wcAsKeakwHg/vvvnyTb/bJmAADY4sQTT8xBBx2UzZs357LLLpv+/fOe97zss88+2/z+sT75yU9m8+bNGR0dzfOe97x+Jeekk07KyMhIJicn89GPfnSH605OTm71xGMAgH6bkwHgr/7qryZJ7rzzzrnYPQAADTI0NJTf+Z3fSZJ85Stfmf4z5IIFC6bvDvzc5z6Xj370o3nwwQeTJBMTE/nIRz6Sa6+9Nkny+te/PvPnz+9b8+joaP7gD/4gSXL11Vfnfe97X372s59NL9+0aVNWrFiRv/u7v8vrXve67T6gBACgH+bkISCvec1rsnTp0nz2s5/Nu971rrk4BAAADfLSl740n/jEJ7J27dp86lOfyh//8R8nefTPlatXr84111yTT3/607niiiuycOHCbNiwYfpBHK94xSvyyle+su/NZ5xxRjZu3Ji/+Zu/yVe/+tV89atfzfz58zN//vw89NBD030AAKXNyR2Ab3rTm3LUUUflm9/8Zv7yL/9yLg4BAECD7LXXXjnrrLOSJP/8z/+cn//850mSVquVd7/73bnoooty/PHHZ3R0NA8//HBGR0dzwgkn5OKLL8473/nOtFqtIt1nnXVWPvGJT+SVr3xlDj744AwNDWXDhg154hOfmGc+85n5vd/7vXzsYx/LokWLivQBACRJq9vtdudix//2b/+WV73qVVm+fHnOPPPMvO1tb8sJJ5yQvffeey4Ox+NYsWJF6QQAAAAAdtOuPPhsTgaAhx56aJJkamoqd9111/T/I9tut/OkJz0pw8PDO45qtfLTn/6011kDzQAQAAAAoH67MgCck+8AvOOOO6aHflv+3u12s2nTptxzzz2Pu32pj3AAAAAAQNPMyQBwyZIlhngAAAAAsAeYszsAAQBojltvvTUXXHDBTm3z1re+NS9+8YvnqAgAgNmakwEgAADNsmnTpoyPj+/UNo888sgc1QAAsDPm7CnA7Fk8BAQAAACgfnvMQ0B+2f33359rrrkmN998c9asWZMHH3ww++yzT5761KfmN37jN3LaaaflSU96Uj9SAAAAAGCgzOkdgA8++GD+6I/+KJ/4xCcyNTW13fXmz5+fN77xjbn44ovzhCc8Ya5yBpo7AAEAAADqtyt3AM7ZAHDVqlV58YtfnNtvvz2zOUSr1cqhhx6ar371qznooIPmImmgGQACAAAA1G+PGQA+8sgjefazn50f//jHSZInPOEJed3rXpeTTz45RxxxRBYuXJgNGzZk5cqV+ed//udcfvnlefDBB5Mkz3jGM7J8+fLstddevc4aaAaAAAAAAPXbYwaAH/zgB/Pud787rVYrxx13XP7xH/8xT33qU7e7/l133ZWzzjorX//619NqtfLf/tt/yzve8Y5eZw00A0AAAACA+u3KAHBoDjryD//wD0mSAw88MF/84hd3OPzbst71118/vd4VV1wxF1kAAAAAMHDmZAD4k5/8JK1WK2984xszOjo6q2322WefnHvuuel2u/nJT34yF1kAAAAAMHDmzcVOH3nkkSTJUUcdtVPb/dqv/VqS5Be/+EXPmwbdokWL+nas0dHRtNvtdDqdTExM9O24vVJzv/Zyau7XXk7N/drLqblfezk192svp+Z+7eXU3K+9jJrbk3r65+Q7AJ/+9Kfntttuy9/+7d/mDW94w6y3+8QnPpE3vvGNOfLII6cfIAIAAAAA7Lo5uQPwJS95SVasWJGvfvWrOzUA/MpXvpJWq5WXvvSlc5E10MbHx/t2rFqm39tTc7/2cmru115Ozf3ay6m5X3s5NfdrL6fmfu3l1NyvvYya25My/WNjYzu9zZwMAN/2trfl7/7u7/KZz3wmb3rTm/Kbv/mbj7vN1772tVxxxRUZGRnJ2972trnIGmidTmegjtsrNfdrL6fmfu3l1NyvvZya+7WXU3O/9nJq7tdeTs392suouT3Zs/vn5CEgRx55ZD7+8Y9n3rx5efnLX55LL710+nsBf9kvfvGLfPjDH86pp56avfbaKx//+MdzxBFHzEUWAAAAAAycObkD8MILL0zy6EeBr7322rztbW/Le9/73rzgBS/IEUcckYULF2bDhg1ZuXJlvva1r+WBBx5Ikpx22mn54Q9/OL39TC644IK5SAYAAACARpqTAeCf/dmfpdVqJcn038fHx3Pttddus263251e59prr51xnccyAAQAAACA2ZuTAWDy6GBvNr/b0e9/2ZZBIQAAAAAwO3MyAPyXf/mXudgtAAAAALCT5mQA+MIXvnAudgsAAAAA7KQ5eQowAAAAALBnmLPvAGyi9evX58orr8zNN9+c+++/P/Pnz89hhx2Wl7/85TnuuON2en+dTie33nprVq5cmZUrV+anP/1p7r777iTJ2Wefnde+9rW9fgsAAAAADBgDwFlatWpVzj///Kxfvz5JMjw8nA0bNmT58uVZvnx5Tj/99LzpTW/aqX2uXbs2733ve+ciFwAAAACSGADOyi9+8Yu8//3vz/r163PwwQfn3e9+dw455JBMTU3l6quvzuWXX55rrrkmhxxySE4++eSd2vfw8HAOPfTQHH744TnssMPymc98JnfdddccvRMAAAAABo0B4CzccMMNufvuuzN//vxccMEFWbRoUZJk/vz5Oeuss7Ju3bpcf/31ueyyy3LSSSdl3rzZndZFixbliiuuSKvVmv7d5z73uTl5DwAAAAAMJg8BmYUbb7wxSXLiiSdOD/8e69WvfnVarVbWrVuX73//+7Pe79DQ0FbDPwAAAADoNQPAxzE5OZnbbrstSfKc5zxnxnUWLVqUxYsXJ0luueWWvrUBAAAAwOMxAHwcd955Z7rdbpLk4IMP3u56W5atXr26L10AAAAAMBsGgI9j3bp10z/vt99+211vy7Lx8fE5bwIAAACA2TIAfBwbN26c/nn+/PnbXW/LssnJyTlvAgAAAIDZMgAEAAAAgAabVzpgT7dgwYLpn6empjIyMjLjelNTU0mS4eHhvnT9sssuuyyf/vSnt7v8d3/3d/Pa1762Ly1DQ0PTfx8bG+vLMXup5n7t5dTcr72cmvu1l1Nzv/Zyau7XXk7N/drLqblfexk1tyf19BsAPo7Hfu/funXrtjsA3PJdgaX+YW/YsCH33nvvdpc//PDDabfbfSxKWq1W34/ZSzX3ay+n5n7t5dTcr72cmvu1l1Nzv/Zyau7XXk7N/drLqLk92fP7DQAfx+LFi9NqtdLtdrNq1aosXrx4xvVWrVqVJDnooIP6mTdt4cKF2X///be7fGRkJJ1Opy8tQ0ND0+ds8+bNfTlmL9Xcr72cmvu1l1Nzv/Zyau7XXk7N/drLqblfezk192svo+b2pEz/rgwaDQAfx/DwcI444oisWLEi3/nOd3LCCSdss87atWuzevXqJMmznvWsficmSc4555ycc845212+du3avj2heGxsLO12O5s3b67yqcg192svp+Z+7eXU3K+9nJr7tZdTc7/2cmru115Ozf3ay6i5PSnT/+QnP3mnt/EQkFk46aSTkiQ33XRT7rvvvm2WX3XVVel2u9lvv/1yzDHH9LkOAAAAALbPAHAWTjnllBxwwAHZuHFj3ve+9+X2229P8uiDP6688spcd911SR69C2/evK1vqjzvvPNyxhln5IMf/OCM+96wYUMmJiam/9pyu+jU1NRWv9/ykBEAAAAA2Bk+AjwLe+21V97znvfk/PPPzx133JF3vOMdGRkZycaNG6cHdqeddlpOPvnknd73n//5n+fWW2/d5vef+9zn8rnPfW769dlnn923p/gCAAAA0BwGgLO0ZMmSXHLJJfnsZz+bm2++OWvXrs3ChQtz6KGH5tRTT81xxx1XOhEAAAAAtmEAuBP23XffnHvuuTn33HNnvc3HPvaxHS6/6KKLdjcLAAAAALbLAHBA7Mojoms+bq/U3K+9nJr7tZdTc7/2cmru115Ozf3ay6m5X3s5NfdrL6Pm9mTP7m91u91u6QgAAAAAYG64A3BAjI+P9+1Yo6Ojabfb6XQ6mZiY6Ntxe6Xmfu3l1NyvvZya+7WXU3O/9nJq7tdeTs392supuV97GTW3J2X6x8bGdnobA8AB0el0Buq4vVJzv/Zyau7XXk7N/drLqblfezk192svp+Z+7eXU3K+9jJrbkz27f6h0AAAAAAAwdwwAAQAAAKDBDAABAAAAoMEMAAEAAACgwQwAAQAAAKDBPAV4QLTb7YE6bq/U3K+9nJr7tZdTc7/2cmru115Ozf3ay6m5X3s5NfdrL6Pm9mTP7m91u91u6QgAAAAAYG64A3BAjI+P9+1Yo6Ojabfb6XQ6mZiY6Ntxe6Xmfu3l1NyvvZya+7WXU3O/9nJq7tdeTs392supuV97GTW3J2X6x8bGdnobA8AB0el0Buq4vVJzv/Zyau7XXk7N/drLqblfezk192svp+Z+7eXU3K+9jJrbkz2730NAAAAAAKDBDAABAAAAoMEMAAEAAACgwQwAAQAAAKDBDAABAAAAoMEMAAEAAACgwQwAAQAAAKDB5pUOoD/a7fZAHbdXau7XXk7N/drLqblfezk192svp+Z+7eXU3K+9nJr7tZdRc3uyZ/e3ut1ut3QEAAAAADA33AE4IMbHx/t2rNHR0bTb7XQ6nUxMTPTtuL1Sc7/2cmru115Ozf3ay6m5X3s5NfdrL6fmfu3l1NyvvYya25My/WNjYzu9jQHggOh0OgN13F6puV97OTX3ay+n5n7t5dTcr72cmvu1l1Nzv/Zyau7XXkbN7cme3e8hIAAAAADQYAaAAAAAANBgBoAAAAAA0GAGgAAAAADQYAaAAAAAANBgBoAAAAAA0GAGgAAAAADQYPNKB9Af7XZ7oI7bKzX3ay+n5n7t5dTcr72cmvu1l1Nzv/Zyau7XXk7N/drLqLk92bP7W91ut1s6AgAAAACYG+4AHBDj4+N9O9bo6Gja7XY6nU4mJib6dtxeqblfezk192svp+Z+7eXU3K+9nJr7tZdTc7/2cmru115Gze1Jmf6xsbGd3sYAcEB0Op2BOm6v1NyvvZya+7WXU3O/9nJq7tdeTs392supuV97OTX3ay+j5vZkz+73EBAAAAAAaDADQAAAAABoMANAAAAAAGgwA0AAAAAAaDADQAAAAABoMANAAAAAAGgwA0AAAAAAaDADQAAAAABoMANAAAAAAGiweaUD6I92uz1Qx+2Vmvu1l1Nzv/Zyau7XXk7N/drLqblfezk192svp+Z+7WXU3J7s2f2tbrfbLR0BAAAAAMwNdwAOiPHx8b4da3R0NO12O51OJxMTE307bq/U3K+9nJr7tZdTc7/2cmru115Ozf3ay6m5X3s5NfdrL6Pm9qRM/9jY2E5vYwA4IDqdzkAdt1dq7tdeTs392supuV97OTX3ay+n5n7t5dTcr72cmvu1l1Fze7Jn93sICAAAAAA0mAEgAAAAADSYASAAAAAANJgBIAAAAAA0mAEgAAAAADSYASAAAAAANJgBIAAAAAA0mAEgAAAAADSYASAAAAAANJgBIAAAAAA02LzSAfRHu90eqOP2Ss392supuV97OTX3ay+n5n7t5dTcr72cmvu1l1Nzv/Yyam5P9uz+Vrfb7ZaOAAAAAADmhjsAB8T4+HjfjjU6Opp2u51Op5OJiYm+HbdXau7XXk7N/drLqblfezk192svp+Z+7eXU3K+9nJr7tZdRc3tSpn9sbGyntzEAHBCdTmegjtsrNfdrL6fmfu3l1NyvvZya+7WXU3O/9nJq7tdeTs392suouT3Zs/s9BAQAAAAAGswAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAabF7pAAAAAIBB8ta7H+jtDnu9v//tQwfsOyf7pf/cAQgAAAAADeYOwAHRbrcH6ri9UnO/9nJq7tdeTs392supuV97OTX3ay+n5n7t5dTcX3N7L/X7PNR+3vfk/la32+2WjgAAAAAYFGffurJ0wqxccfThpRPoEXcADojx8fG+HWt0dDTtdjudTicTExN9O26v1NyvvZya+7WXU3O/9nJq7tdeTs392supuV97OTX319w+V/oxS6j9vJfoHxsb2+ltDAAHRKfTGajj9krN/drLqblfezk192svp+Z+7eXU3K+9nJr7tZdTc3/N7b3U7/NQ+3nfk/s9BAQAAAAAGswAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAazAAQAAAAABrMABAAAAAAGmxe6QD6o91uD9Rxe6Xmfu3l1NyvvZya+7WXU3O/9nJq7tdeTs392supub/m9l7q93mo/bzvyf2tbrfbLR0BAAAAMCjOvnVl6YRZueLow0sn0CPuABwQ4+PjfTvW6Oho2u12Op1OJiYm+nbcXqm5X3s5NfdrL6fmfu3l1NyvvZya+7WXU3O/9nJq7q+5fa70Y5ZQ+3kv0T82NrbT2xgADohOpzNQx+2Vmvu1l1Nzv/Zyau7XXk7N/drLqblfezk192svp+b+mtt7qd/nofbzvif3ewgIAAAAADSYASAAAAAANJgBIAAAAAA0mAEgAAAAADSYASAAAAAANJgBIAAAAAA0mAEgAAAAADSYASAAAAAANJgBIAAAAAA0mAEgAAAAADSYASAAAAAANNi80gH9tH79+lx55ZW5+eabc//992f+/Pk57LDD8vKXvzzHHXfcLu9306ZNufbaa7N06dKsWbMmSfK0pz0tL3zhC3Pqqadm3ryZT/OyZcvygx/8ILfddlvWrl2b9evXp9vtZmxsLE9/+tPzspe9LEcdddQudwEAAADAwAwAV61alfPPPz/r169PkgwPD2fDhg1Zvnx5li9fntNPPz1vetObdnq/k5OTee9735sVK1YkSfbee+8kycqVK7Ny5cp8/etfz4UXXpgFCxZss+0nP/nJ/PznP59+vXDhwkxNTeWee+7JPffck6VLl+YVr3hFzj333F15ywAAAAAwGAPAX/ziF3n/+9+f9evX5+CDD8673/3uHHLIIZmamsrVV1+dyy+/PNdcc00OOeSQnHzyyTu170svvTQrVqzIwoUL8/a3v336TsJly5blr//6r/PjH/84H/7wh/Oud71rm21f8IIXZP/9988znvGMPOUpT8lee+2Vbrebn//85/nHf/zH/Mu//EuuvvrqHHbYYTnppJN6cSoAAAAAGDAD8R2AN9xwQ+6+++7Mnz8/F1xwQQ455JAkyfz583PWWWflt37rt5Ikl112WTZt2jTr/d5+++256aabkiRve9vbcvzxx6fVaqXVauX444/PW9/61iTJjTfemH/7t3/bZvvXve51eclLXpLFixdnr732SpK0Wq0sXrw473znO/Nrv/ZrSZKvfOUru/7mAQAAABhoAzEAvPHGG5MkJ554YhYtWrTN8le/+tVptVpZt25dvv/97896v0uXLk23282BBx6Y448/fpvlJ5xwQg488MB0u90sXbp0p5pbrVaOOOKIJMn999+/U9sCAAAAwBaNHwBOTk7mtttuS5I85znPmXGdRYsWZfHixUmSW265Zdb7/t73vpckOfbYY9NqtbZZ3mq1cuyxx2617mxt3rw5P/nJT5IkBxxwwE5tCwAAAABbNP47AO+88850u90kycEHH7zd9Q4++OCsXr06q1evntV+u91u7rzzzsfd75IlS5Jk1vt96KGHcuedd+bzn/98fvzjHydJTjvttFltCwAAAAC/rPEDwHXr1k3/vN9++213vS3LxsfHZ7XfycnJbNy4cdb7nZyczOTkZIaHh7dZZ9myZbnooou2+f3ChQtz3nnnbffORQAAAAB4PI3/CPCWIV3y6EM/tmfLssnJyVnt97HrzWa/O9r3XnvtlX333TdPfOITMzT06D+S4eHhvP71r88LXvCCWfUAAAAAwEwafwdgDZ773Ofm7//+75MkmzZtyh133JFPf/rTufTSS/OlL30pF1xwwQ7vMgQAAACA7Wn8AHDBggXTP09NTWVkZGTG9aamppJkxo/ozuSx623Zdkf7ne2+582bl8MPPzwXXHBBLrrooixbtiwf+chH8id/8ic73O6yyy7Lpz/96e0u/93f/d289rWvfdzj98KWuxiHhoYyNjbWl2P2Us392supuV97OTX3ay+n5n7t5dTcr72cmvu1l1Nzf1/b735gbvffI/34Z1jzNZPU09/4AeBj75xbt27ddgeAW74rcLb/sIaHhzM8PJzJycmtvmdwe/vdsv7OOP3007Ns2bIsW7YsExMTGR0d3e66GzZsyL333rvd5Q8//HDa7fZOHX93tVqtvh+zl2ru115Ozf3ay6m5X3s5NfdrL6fmfu3l1NyvvZya+2tu77V+nofaz/ue3t/4AeDixYvTarXS7XazatWqLF68eMb1Vq1alSQ56KCDZrXfVquVxYsX57bbbpvethf7fawnPelJ0z/ffffdOxwALly4MPvvv/92l4+MjKTT6ex0w64YGhqaPuebN2/uyzF7qeZ+7eXU3K+9nJr7tZdTc7/2cmru115Ozf3ay6m5v+b2udKPOULt571E/64MGhs/ABweHs4RRxyRFStW5Dvf+U5OOOGEbdZZu3ZtVq9enSR51rOeNet9P/OZz8xtt92W7373u9tdZ/ny5dPr7qy77757+ufHfpR5Juecc07OOeec7S5fu3btrJ9wvLvGxsbSbrezefPmvh2zl2ru115Ozf3ay6m5X3s5NfdrL6fmfu3l1NyvvZya+2tunyv9OA+1n/cS/U9+8pN3epvGPwU4SU466aQkyU033ZT77rtvm+VXXXVVut1u9ttvvxxzzDGz3u+JJ56YVquVNWvW5Jvf/OY2y7/xjW9kzZo1abVa0w1bPN4UvdPp5POf/3yS5IlPfOJ271wEAAAAgB0ZiAHgKaeckgMOOCAbN27M+973vtx+++1JHn1Ax5VXXpnrrrsuyaN30c2bt/VNkeedd17OOOOMfPCDH9xmv4ccckhOPPHEJMkll1ySZcuWpdvtptvtZtmyZfnQhz6U5NEB5JIlS7ba9sYbb8yf//mf51vf+lYefPDB6d9v2rQpP/jBD/Jnf/Zn03cP/s7v/M70l0oCAAAAwM5o/EeAk2SvvfbKe97znpx//vm544478o53vCMjIyPZuHHj9OezTzvttJx88sk7ve+3vOUtueuuu7JixYpcdNFF2XvvvZMkjzzySJLk6U9/et785jfPuO23vvWtfOtb30ry6EeV582bl4cffnj67sChoaH89m//dk477bSd7gIAAACAZEAGgEmyZMmSXHLJJfnsZz+bm2++OWvXrs3ChQtz6KGH5tRTT81xxx23S/sdHh7OxRdfnGuvvTZLly7NmjVrkiSHHXZYTjrppJx66qnb3FWYJM973vPylre8Jd///vdzxx135IEHHsjDDz+cBQsW5IADDshRRx2Vl770pdvcOQgAAAAAO2NgBoBJsu++++bcc8/NueeeO+ttPvaxjz3uOvPmzcuZZ56ZM888c9b7feITn5iXvexlednLXjbrbXZHqUdR78mPwJ6Nmvu1l1Nzv/Zyau7XXk7N/drLqblfezk192svp+b+mtt7qd/nofbzvif3t7rdbrd0BAAAAMCgOPvWlaUTZuWKow8vnUCPDNQdgIOsn4/SHh0dTbvdTqfTycTERN+O2ys192svp+Z+7eXU3K+9nJr7tZdTc7/2cmru115Ozf01t8+VfswSaj/vJfrHxsZ2ehsDwAGx5cEig3LcXqm5X3s5NfdrL6fmfu3l1NyvvZya+7WXU3O/9nJq7q+5vZf6fR5qP+97cv9Q6QAAAAAAYO4YAAIAAABAgxkAAgAAAECDGQACAAAAQIMZAAIAAABAg3kK8IBot9sDddxeqblfezk192svp+Z+7eXU3K+9nJr7tZdTc7/2cmrur7m9l/p9Hmo/73tyf6vb7XZLRwAAAAAMirNvXVk6YVauOPrw0gn0iDsAB8T4+HjfjjU6Opp2u51Op5OJiYm+HbdXau7XXk7N/drLqblfezk192svp+Z+7eXU3K+9nJr7a26fK/2YJdR+3kv0j42N7fQ2BoADotPpDNRxe6Xmfu3l1NyvvZya+7WXU3O/9nJq7tdeTs392supub/m9l7q93mo/bzvyf0eAgIAAAAADWYACAAAAAANZgAIAAAAAA1mAAgAAAAADWYACAAAAAAN5inAA6Ldbg/UcXul5n7t5dTcr72cmvu1l1Nzv/Zyau7XXk7N/drLqbm/5vZe6vd5qP2878n9rW632y0dAQAAADAozr51ZemEWbni6MNLJ9Aj7gAcEOPj43071ujoaNrtdjqdTiYmJvp23F6puV97OTX3ay+n5n7t5dTcr72cmvu1l1Nzv/Zyau6vuX2u9GOWUPt5L9E/Nja209sYAA6ITqczUMftlZr7tZdTc7/2cmru115Ozf3ay6m5X3s5NfdrL6fm/prbe6nf56H2874n93sICAAAAAA0mAEgAAAAADSYASAAAAAANJgBIAAAAAA0mAEgAAAAADSYASAAAAAANJgBIAAAAAA02LzSAfRHu90eqOP2Ss392supuV97OTX3ay+n5n7t5dTcr72cmvu1l1Nzf83tvdTv81D7ed+T+1vdbrdbOgIAAABgUJx968rSCbNyxdGHl06gR9wBOCDGx8f7dqzR0dG02+10Op1MTEz07bi9UnO/9nJq7tdeTs392supuV97OTX3ay+n5n7t5dTcX3P7XOnHLKH2816if2xsbKe3MQAcEJ1OZ6CO2ys192svp+Z+7eXU3K+9nJr7tZdTc7/2cmru115Ozf01t/dSv89D7ed9T+73EBAAAAAAaDADQAAAAABoMANAAAAAAGgwA0AAAAAAaDADQAAAAABoMANAAAAAAGgwA0AAAAAAaDADQAAAAABosHmlA+iPdrs9UMftlZr7tZdTc7/2cmru115Ozf3ay6m5X3s5NfdrL6fm/prbe6nf56H2874n97e63W63dAQAAADAoDj71pWlE2bliqMPL51Aj7gDcECMj4/37Vijo6Npt9vpdDqZmJjo23F7peZ+7eXU3K+9nJr7tZdTc7/2cmru115Ozf3ay6m5v+b2udKPWULt571E/9jY2E5vYwA4IDqdzkAdt1dq7tdeTs392supuV97OTX3ay+n5n7t5dTcr72cmvtrbu+lfp+H2s/7ntzvISAAAAAA0GAGgAAAAADQYAaAAAAAANBgBoAAAAAA0GAGgAAAAADQYAaAAAAAANBgBoAAAAAA0GAGgAAAAADQYAaAAAAAANBg80oH0B/tdnugjtsrNfdrL6fmfu3l1NyvvZya+7WXU3O/9nJq7tdeTs39Nbf3Ur/PQ+3nfU/ub3W73W7pCAAAAIBBcfatK0snzMoVRx9eOoEecQfggBgfH+/bsUZHR9Nut9PpdDIxMdG34/ZKzf3ay6m5X3s5NfdrL6fmfu3l1NyvvZya+7WXU3N/ze1zpR+zhNrPe4n+sbGxnd7GAHBAdDqdgTpur9Tcr72cmvu1l1Nzv/Zyau7XXk7N/drLqblfezk199fc3kv9Pg+1n/c9ud9DQAAAAACgwQwAAQAAAKDBDAABAAAAoMEMAAEAAACgwQwAAQAAAKDBDAABAAAAoMEMAAEAAACgwQwAAQAAAKDBDAABAAAAoMEMAAEAAACgwQwAAQAAAKDBDAABAAAAoMHmlQ6gP9rt9kAdt1dq7tdeTs392supuV97OTX3ay+n5n7t5dTcr72cmvtrbu+lfp+H2s/7ntzf6na73dIRAAAAAIPi7FtXlk6YlSuOPrx0Aj3iDsABMT4+3rdjjY6Opt1up9PpZGJiom/H7ZWa+7WXU3O/9nJq7tdeTs392supuV97OTX3ay+n5v6a2+dKP2YJtZ/3Ev1jY2M7vY0B4IDodDoDddxeqblfezk192svp+Z+7eXU3K+9nJr7tZdTc7/2cmrur7m9l/p9Hmo/73tyv4eAAAAAAECDGQACAAAAQIMZAAIAAABAgxkAAgAAAECDGQACAAAAQIMZAAIAAABAgxkAAgAAAECDGQACAAAAQIMZAAIAAABAgxkAAgAAAECDGQACAAAAQIMZAAIAAABAgxkAAgAAAECDzSsdQH+02+2BOm6v1NyvvZya+7WXU3O/9nJq7tdeTs392supuV97OTX319zeS/0+D7Wf9z25v9XtdrulIwAAAAAGxdm3riydMCtXHH146QR6xB2AA2J8fLxvxxodHU273U6n08nExETfjtsrNfdrL6fmfu3l1NyvvZya+7WXU3O/9nJq7tdeTs39NbfPlX7MEmo/7yX6x8bGdnobA8AB0el0Buq4vVJzv/Zyau7XXk7N/drLqblfezk192svp+Z+7eXU3F9zey/1+zzUft735H4PAQEAAACABjMABAAAAIAGMwAEAAAAgAYzAAQAAACABjMABAAAAIAGMwAEAAAAgAYzAAQAAACABjMABAAAAIAGm1c6oJ/Wr1+fK6+8MjfffHPuv//+zJ8/P4cddlhe/vKX57jjjtvl/W7atCnXXnttli5dmjVr1iRJnva0p+WFL3xhTj311MybN/Npvueee/LDH/4wK1euzE9/+tP87Gc/y8aNG5MkX/jCF3a5BwAAAAC2GJgB4KpVq3L++edn/fr1SZLh4eFs2LAhy5cvz/Lly3P66afnTW96007vd3JyMu9973uzYsWKJMnee++dJFm5cmVWrlyZr3/967nwwguzYMGCbbb9zGc+k69+9au78a4AAAAAYMcGYgD4i1/8Iu9///uzfv36HHzwwXn3u9+dQw45JFNTU7n66qtz+eWX55prrskhhxySk08+eaf2femll2bFihVZuHBh3v72t0/fSbhs2bL89V//dX784x/nwx/+cN71rndts22r1cqBBx6Yww8/PIcddlgmJyfzD//wDz15zwAAAACQDMgA8IYbbsjdd9+d+fPn54ILLsiiRYuSJPPnz89ZZ52VdevW5frrr89ll12Wk046absf2f1lt99+e2666aYkydve9rYcf/zx08uOP/74bN68OR/4wAdy44035lWvelUOPvjgrbZ/61vfmna7Pf162bJlu/tWAQAAAGArA/EQkBtvvDFJcuKJJ04P/x7r1a9+dVqtVtatW5fvf//7s97v0qVL0+12c+CBB241/NvihBNOyIEHHphut5ulS5dus/yxwz8AAAAAmAuNHwBOTk7mtttuS5I85znPmXGdRYsWZfHixUmSW265Zdb7/t73vpckOfbYY9NqtbZZ3mq1cuyxx261LgAAAAD0U+MHgHfeeWe63W6SbPMR3Mfasmz16tWz2m+3282dd975uPtdsmTJTu0XAAAAAHqp8QPAdevWTf+83377bXe9LcvGx8dntd/Jycls3Lhx1vudnJzM5OTkrPYNAAAAAL3S+AHgliFd8uhDP7Zny7LZDukeu95s9rsz+wYAAACAXmn8ABAAAAAABtm80gFzbcGCBdM/T01NZWRkZMb1pqamkiTDw8Oz2u9j19uy7Y72uzP73hWXXXZZPv3pT293+e/+7u/mta997Zwd/7GGhoam/z42NtaXY/ZSzf3ay6m5X3s5NfdrL6fmfu3l1NyvvZya+7WXU3N/X9vvfmBu998j/fhnWPM1k9TT3/gB4GO/n2/dunXbHQBu+a7A2f7DGh4ezvDwcCYnJ7f6nsHt7XfL+nNlw4YNuffee7e7/OGHH0673Z6z48+k1Wr1/Zi9VHO/9nJq7tdeTs392supuV97OTX3ay+n5n7t5dTcX3N7r/XzPNR+3vf0/sYPABcvXpxWq5Vut5tVq1Zl8eLFM663atWqJMlBBx00q/22Wq0sXrw4t9122/S2vdjvrlq4cGH233//7S4fGRlJp9OZ04YthoaGps/55s2b+3LMXqq5X3s5NfdrL6fmfu3l1NyvvZya+7WXU3O/9nJq7q+5fa70Y45Q+3kv0b8rg8bGDwCHh4dzxBFHZMWKFfnOd76TE044YZt11q5dm9WrVydJnvWsZ81638985jNz22235bvf/e5211m+fPn0unPpnHPOyTnnnLPd5WvXrp31E45319jYWNrtdjZv3ty3Y/ZSzf3ay6m5X3s5NfdrL6fmfu3l1NyvvZya+7WXU3N/ze1zpR/nofbzXqL/yU9+8k5vMxAPATnppJOSJDfddFPuu+++bZZfddVV6Xa72W+//XLMMcfMer8nnnhiWq1W1qxZk29+85vbLP/GN76RNWvWpNVqTTcAAAAAQD8NxADwlFNOyQEHHJCNGzfmfe97X26//fYkjz6g48orr8x1112X5NG76ObN2/qmyPPOOy9nnHFGPvjBD26z30MOOSQnnnhikuSSSy7JsmXL0u120+12s2zZsnzoQx9K8ugAcsmSJdtsv2nTpkxMTEz/NTk5Ob3ssb+fmJjoyXkAAAAAYPA0/iPASbLXXnvlPe95T84///zccccdecc73pGRkZFs3Lhx+vPZp512Wk4++eSd3vdb3vKW3HXXXVmxYkUuuuii7L333kmSRx55JEny9Kc/PW9+85tn3PZHP/pRzj///BmX/fLHeb/whS/sdBsAAAAADMQAMEmWLFmSSy65JJ/97Gdz8803Z+3atVm4cGEOPfTQnHrqqTnuuON2ab/Dw8O5+OKLc+2112bp0qVZs2ZNkuSwww7LSSedlFNPPXWbuwoBAAAAoF8GajK177775txzz8255547620+9rGPPe468+bNy5lnnpkzzzxzp3qOOeaYvt3ZV+pR1HvyI7Bno+Z+7eXU3K+9nJr7tZdTc7/2cmru115Ozf3ay6m5v+b2Xur3eaj9vO/J/a1ut9stHQEAAAAwKM6+dWXphFm54ujDSyfQIwN1B+Ag6+ejtEdHR9Nut9PpdKp8gEnN/drLqblfezk192svp+Z+7eXU3K+9nJr7tZdTc3/N7XOlH7OE2s97if6xsbGd3sYAcEB0Op2BOm6v1NyvvZya+7WXU3O/9nJq7tdeTs392supuV97OTX319zeS/0+D7Wf9z25f6h0AAAAAAAwdwwAAQAAAKDBDAABAAAAoMEMAAEAAACgwQwAAQAAAKDBDAABAAAAoMHmlQ6gP9rt9kAdt1dq7tdeTs392supuV97OTX3ay+n5n7t5dTcr72cmvtrbu+lfp+H2s/7ntzf6na73dIRAAAAAIPi7FtXlk6YlSuOPrx0Aj3iDsABMT4+3rdjjY6Opt1up9PpZGJiom/H7ZWa+7WXU3O/9nJq7tdeTs392supuV97OTX3ay+n5v6a2+dKP2YJtZ/3Ev1jY2M7vY0B4IDodDoDddxeqblfezk192svp+Z+7eXU3K+9nJr7tZdTc7/2cmrur7m9l/p9Hmo/73tyv4eAAAAAAECDGQACAAAAQIMZAAIAAABAgxkAAgAAAECDGQACAAAAQIMZAAIAAABAg80rHUB/tNvtgTpur9Tcr72cmvu1l1Nzv/Zyau7XXk7N/drLqblfezk199fc3kv9Pg+1n/c9ub/V7Xa7pSMAAAAABsXZt64snTArVxx9eOkEesQdgANifHy8b8caHR1Nu91Op9PJxMRE347bKzX3ay+n5n7t5dTcr72cmvu1l1Nzv/Zyau7XXk7N/TW3z5V+zBJqP+8l+sfGxnZ6GwPAAdHpdAbquL1Sc7/2cmru115Ozf3ay6m5X3s5NfdrL6fmfu3l1Nxfc3sv9fs81H7e9+R+DwEBAAAAgAYzAAQAAACABjMABAAAAIAGMwAEAAAAgAYzAAQAAACABjMABAAAAIAGMwAEAAAAgAYzAAQAAACABptXOoD+aLfbA3XcXqm5X3s5NfdrL6fmfu3l1NyvvZya+7WXU3O/9nJq7q+5vZf6fR5qP+97cn+r2+12S0cAAAAADIqzb11ZOmFWrjj68NIJ9Ig7AAfE+Ph43441OjqadrudTqeTiYmJvh23V2ru115Ozf3ay6m5X3s5NfdrL6fmfu3l1NyvvZya+2tunyv9mCXUft5L9I+Nje30NgaAA6LT6QzUcXul5n7t5dTcr72cmvu1l1Nzv/Zyau7XXk7N/drLqbm/5vZe6vd5qP2878n9HgICAAAAAA1mAAgAAAAADWYACAAAAAANZgAIAAAAAA1mAAgAAAAADWYACAAAAAANZgAIAAAAAA1mAAgAAAAADTavdAD90W63B+q4vVJzv/Zyau7XXk7N/drLqblfezk192svp+Z+7eXU3F9zey/1+zzUft735P5Wt9vtlo4AAAAAGBRn37qydMKsXHH04aUT6BF3AA6I8fHxvh1rdHQ07XY7nU4nExMTfTtur9Tcr72cmvu1l1Nzv/Zyau7XXk7N/drLqblfezk199fcPlf6MUuo/byX6B8bG9vpbQwAB0Sn0xmo4/ZKzf3ay6m5X3s5NfdrL6fmfu3l1NyvvZya+7WXU3N/ze291O/zUPt535P7DQABAAAAGAhvvfuB3u6w1/v73z50wL493Z+nAAMAAABAg7kDEAAA9iA9vzMhqebuBICdMah3csGuMAAEAPZotfzhPvEHfADq4v9wYFf4s1mdDACp+j+8tf8Lq5ZzX3N74rp5XNpn5Lp5HP5wPyuum1+ifUau+8fhupmRf089Du3baNp/1wA7x3cAAgAAAECDGQACAAAAQIMZAAIAAABAgxkAAgAAAECDGQACAAAAQIMZAAIAAABAg80rHUB/tNvt0gk9UfP70F5Ozf3ay6m5X3s5NfdrL6fmfu3l1NyvvYya25O6+7WXU3N/r9sNAAfE2NjY9hf+/P7+heymbd5Hze1JNf01tyeum1Jqbk9cN6XU3J64bkqpuT1x3ZRSc3viuilFezk192svp+b+Hc5xdoEB4IAYHx8vndATNb8P7eXU3K+9nJr7tZfz/7d353FV1vn7x69bUHbXREeUInE3EpcaywS1ci00M9TcKrWxmh46TU7rTLbM2LcZy74tI7aoWfktJW0S1/wBoqbkrpMYmoqaGyoo4BHh/P7wwRmRRXCEz7mPr+dfxH3fxzdXcM65r3Mvdp6f2c2x8/zMbo6d52d2M+w8u2Tv+ZndHDvPX97sV1MOUgBeJwoKCkyPcE3Y+edgdnPsPD+zm2Pn+ZndHDvPz+zm2Hl+ZjfHzvMzuxl2nl2y9/zMbo6d57/Ws3MTEAAAAAAAAMCDUQACAAAAAAAAHowCEAAAAAAAAPBgFIAAAAAAAACAB6MABAAAAAAAADyY5XQ6naaHgGeZO3eucnJyFBAQoBEjRpgep9LsPD+zm2Pn+ZndHDvPz+zm2Hl+ZjfHzvMzuzl2np/ZzbHz/Mxuhp1nl+wzPwUgrrl+/frp2LFjCg4OVkJCgulxKs3O8zO7OXaen9nNsfP8zG6OnedndnPsPD+zm2Pn+ZndHDvPz+xm2Hl2yT7zcwowAAAAAAAA4MEoAAEAAAAAAAAPRgEIAAAAAAAAeDAKQAAAAAAAAMCDUQACAAAAAAAAHowCEAAAAAAAAPBg3qYHgOcZPny4cnJyFBAQYHqUq2Ln+ZndHDvPz+zm2Hl+ZjfHzvMzuzl2np/ZzbHz/Mxujp3nZ3Yz7Dy7ZJ/5LafT6TQ9BAAAAAAAAICqwSnAAAAAAAAAgAejAAQAAAAAAAA8GAUgAAAAAAAA4MEoAAEAAAAAAAAPRgEIAAAAAAAAeDAKQAAAAAAAAMCDeZseAPZ39uxZnTx5Unl5eZIkPz8/1a9fX4GBgYYnAwBUpQsXLmj27NmSpMcee8zwNEDVKSgoUHp6uk6cOCHLshQcHKzmzZvLsizTowFV7vTp0/Lz85OPj4/pUTyaw+FQbm6uatSooYCAAHl7s6sO4NqynE6n0/QQsJ+NGzcqMTFR27ZtU1ZWVqnr1KlTRxEREYqOjlanTp2qecKK8/QC89y5c5oxY4Ysy9LTTz9tehzbys/P18GDB1VQUKAmTZrI39//itukpKTo/Pnz6tmzZzVM6Jlyc3OVmpqqzMxMNW7cWF26dFHNmjVdyzds2KDly5fryJEj8vX1Vfv27XX//ferfv36Bqe+fpw7d06xsbGyLEsLFy40PU6l2a3A3LJli1JTU3XkyBFJUqNGjdSlSxdFRkYanszesrOzdejQIQUFBalp06bFljmdTs2fP18LFy5UTk5OsWV169bV8OHDde+991bnuFfFXQvMl156SeHh4erVq5eaNWtmdJbr0bp167R27Vrl5eUpPDxc9913nwICAiRJx44d0+zZs5Wamqrz589LkkJDQxUTE6NevXqZHLvS3LXAPHz4sBITE7VlyxZlZGS49kWK3HDDDWrdurWio6PVuXNnQ1NWnN0KzMOHD2vbtm3KyMgodV+wWbNmioiIUJMmTQxPev1yOp06ceKEJKlhw4aGp/EMFIColKysLP3P//yPdu7cKeniH2V5it5YtmvXTs8++6zq1q1b1SNWiCcVmFeSnZ2tkSNH2nYH3XSBWVhYqLlz52rx4sVyOBySJC8vL91xxx0aNWpUuS9Go0ePVlZWllvkbscCc+fOnZo6darOnDnj+l5wcLBee+01NWrUSLNnz9Y333xT7HnIsiz5+/vrL3/5i1q1amVi7GI8vcC0ewHoTvMvXLhQvr6+6tOnT4llubm5evPNN7V161ZJ/3ntLXqN7dChgyZPnlyhv2uT3LXAnDt3rubPn69hw4YpNja22LK33npLa9asKfP9jmVZiomJ0SOPPFIdo5bKzgVmTEyM6/c4PDxcd999t+666y5XCeXu7FxgxsXFKSEhQdLF3xPLstSwYUP94x//kMPh0OTJk3Xq1KkSv/uWZalfv34aP368ibGLsWuBWVBQoJkzZ2r58uUqLCys0P7UzTffrIkTJyo0NLSaprwyuxaY27dv1+zZs5Wenu76Xmm/50VatGihUaNG6ZZbbqm2GSvieigw7b4f644FJgUgKszhcGjixIn69ddfJUm33HKLOnbsqNDQUNWvX9/1qZrD4dDJkyd14MABbd68Wdu2bZMkhYSEaNq0aUY/ffOUArMy7P7EaXr+v//970pJSSn1jYG/v7+efvpp/fa3vy11W3coAO1aYJ4+fVpPPPGEa4c1MDBQZ8+elXTxjdi4ceM0efJk+fn5qWvXrmrQoIGOHDmi9evX6/z582rQoIE++OAD+fr6VvvsRexaYA4cOPCaPI4dnm/cqQCMiYlRvXr1NGvWrGLfLyws1Msvv6ydO3fK6XSqYcOGatGihSTp559/1vHjx2VZlm699VZNmTLFwOQX2bnA/NOf/qS0tDS9++67xXauk5KSNG3aNElS165dNXDgQFfBdvDgQcXHx2v9+vWyLEuvvvqqIiIijMxv5wKzqAC89HfC29tbv/3tb9WzZ09FRkYaP0qxPHYtMLdu3ao///nPkqSwsDA1b95c6enp2rdvn+655x7l5+crMTFRTZs2Vd++fdW4cWMdP35cK1as0J49e2RZlqZMmaJbb73V2M9g5wJzypQp2rx5s5xOp2rXrq2QkBAVFBTo8OHDOnv2rLy9vTVo0CDVqFFDe/fu1ZYtW5Sfny8/Pz9NmTLF+Aecdi4wFy5cqFmzZrlm9vf3V0hISKn7socOHVJubq6kiz/DI488opiYGGOzF/GUArMiTO8H/rfccX73Pi4XbiU+Pl6HDx/WDTfcoOeee861A1KasLAwderUSYMGDVJ6err+9re/6dChQ/rmm280dOjQapz6PxwOh5577rmrKjB37typF1980XiBieqVmpqq1atXy7Is9ejRQ3fffbdq166t7du3a8GCBcrMzNTUqVP1u9/9rtSdXncwbdq0EgXmhQsXtHr1am3cuLHcAtOk7777Tjk5OQoJCdHLL7+s3/zmN8rIyNBrr72m9PR0zZw5Uw0bNtTUqVN1ww03uLY7dOiQXnjhBZ08eVKrVq1Sv379jMx/+vRpvfHGGyUKzKNHj+qtt97SuHHjFB8fX2qBmZOTozfffNNYgXktPhc0ucNe2QKzaNbLt3OXN2qrV6/Wjh07VKNGDY0bN079+vVzzex0OpWQkKCZM2dq69at2rBhg2677TYjc3766aeqV69eiefCwsJCvfHGG+UWmFu2bNGbb75prMA8evSopIsfVF5q5cqVsixLgwYN0ujRo4sta926tV544QXNmjVL33zzjRYvXmysANy+fbukiyXlpZKSkpSSkuJaVlaBuWjRInXq1MnY/EFBQRo7dqy+//57bdu2Tfn5+UpJSVFKSorq16+vHj16qGfPniX+/7gTp9Opn3/+Wenp6froo4/cvsBctmyZJCk6OloTJ050lbBvv/221qxZo/z8fLVs2VJ//etfix21fu+99+qNN97Qxo0btWzZMmMF4NatW7V48WJJJQvMOXPmKD8/XydPniyzwExISNDtt99uZP6VK1dq06ZN8vf319ixY9WzZ0/X70hBQYGWLFmiWbNmKSUlRW+//bb8/PyUnZ2tGTNmKCUlRVOnTtUHH3wgPz+/ap+9yOuvv17pAnPPnj2aPHmy0QJzx44drvKvc+fOGjx4sFq3bq0aNUq/L6rT6dSuXbu0YMECpaam6tNPP1WLFi3Utm3bap78P66mwNy9e7defvlltykwYRYFICps7dq1sixLf/jDH8ot/y4XHh6uSZMm6cUXX9SaNWuMFYB2LjCnT59+1dteuHDhGk5yfVmxYoUsy1Lv3r01YcIE1/dDQ0PVq1cv/e///q9SUlL0z3/+Uw6Hw+1eVO1cYG7atEmWZenRRx/Vb37zG0lSs2bN9PDDD2vatGlKT0/XpEmTipV/0sUd+Icffljvv/++NmzYYKwAtHuBaVmWWrZsqd69e5e7Xn5+vj788ENZlqXf//731TRd+exeYF4uKSlJlmVpwIAB6t+/f7FllmWpf//+OnbsmBYuXKjExERjBWBZ7FBgnjlzRgEBAfLy8ir2/b1798qyLD344INlbjtkyBAtXLhQaWlpVT1mmexeYHp5eSkqKkpRUVHKzMzU999/r//3//6fDh8+rMzMTC1YsEALFixQy5Ytdffdd6tbt25udbq7HQvMtLQ0WZalESNGuP4ei/676Dln5MiRxco/6eL/qzFjxmjjxo3avXu3idEl2bvA/P7772VZlp544gndddddxZZ5eXlpwIABKiws1Mcff6xFixZp6NChql27tp599lnl5uZq8+bNSkhI0ODBg6t9dsneBea3334rp9OpAQMGaNy4cVdc37IstWnTRi+99JJmzpyp7777TosWLTJWANq1wDx+/PhVb1t09g+uHQpAVNjRo0fl4+Ojdu3aVXrb9u3by9fXV8eOHauCySrGzgXmqlWr3GqHtLLsWmD+/PPPkqSHHnqoxDJfX189++yzCgkJ0f/93//p008/lcPhKHVdU+xcYBYdqduhQ4di37/09IUuXbqUuu0dd9yh999/X/v376+y+a7EzgXmc889p7i4ONfO3YQJExQWFlbquufOndOHH34oScavqXQpOxeYl9uzZ48k6b777itznf79+2vhwoWu5yx3YocCMygoSFlZWbpw4UKxi9Y7HA75+/uXezpnQECAAgICip3qX93sXmBeqkGDBnrooYf00EMPadeuXVq5cqXWrFmj3NxcpaWlaffu3froo4/UtWtX9ezZ0+gpqEXsWGBmZWXJx8enxGVAgoOD5evrK4fDoebNm5e6bbNmzVSzZk2dPn26GiYtnZ0LzAMHDsjb21t33HFHmetER0fr448/1o8//lhsv2P48OHatGmT1q1bZ6wAtHOBWfR78/DDD1d624cffliLFy/Wrl27qmCyirFrgTl27Fhb78d6WoFJAYgK8/b2lsPhUEFBQYk3mVdSUFCgCxcuGD191s4FZtGTZuvWrVW7du1KbXvhwgVt3LixKsaqMLsWmFlZWfLz81ODBg3KXGf48OHy8/PTrFmz9MUXX8jhcGjkyJHVOGXZ7FxgOhyOUu8gV3QdzoCAgDJ3oAIDA+Xv7290h9zOBWbXrl116623avbs2Vq2bJmeeeYZDRgwQMOHDzd6TcWK8oQC81JnzpyRr6+vgoODy1wnODhYfn5+RnfIy2KHAjMsLEybN2/W5s2bi/1dNmrUSIcPH1Zubm6Zzze5ubnKzc1VnTp1qmvcEuxeYJaldevWat26tcaPH69169a5jrBzOBxKSkpSUlKSbrjhBvXs2VPDhw83Pa4kexWYBQUF5X6/rA9gCwsLVVhYWOZRR9XBzgWmw+GQj49PuftSRUfHXT5jixYt5O3t7XqPYYKdC8ycnBz5+fldVQHv7+8vPz+/EjdTqk52LjDtfNsJuxeYl6MARIWFhoZq165dWrVqle65555Kbbtq1SpduHChUkfeXWt2LjBDQkJ06NAh3XPPPZXeSS26+KhJdi0wa9asqfz8/CuuN2jQINWsWVMzZ87UggULdP78eT322GPVMGH57FxgBgUFlbpDWrTDcfmn+qWtV9m/82vJ7gWmv7+/JkyYoOjoaL333ntatGiR1qxZo3HjxrnlNSMvZfcC83IBAQEVeh7y8vJyy0s+2KHA7N69uzZt2qTZs2erbdu2rsIsKipKn3/+ub755psyd7ji4+NVWFho9KL8di8wr6RWrVquI+xOnDjhOsLu119/1fHjx/XVV1+5TQF4KXcuMBs2bKhff/1V+/fv14033uj6/i+//KL8/HxZlqXdu3eX+kHV3r17VVBQUOII9upm1wKzfv36OnbsmI4cOaLGjRuXuk7RBydBQUEllvn4+Lhu6maCnQvMunXr6sSJEzp69KgaNWpUqW2PHDmi3Nxco3dytWuBGRAQoNzcXI0dO7bSB+KcPXtWL730UhVNVnF2LjAvRwGICuvVq5d++uknzZgxQ7m5uerbt69q1apV7jb5+flasmSJ5syZI8uyKl0cXkt2LjDDw8N16NAh7dmzx22PUimPXQvMxo0ba//+/crIyFCzZs3KXXfAgAHy9vbWP//5T/3rX/9Sfn6+8RcLOxeYdevWVVZWlrKyskrsmN52220KDAwsc9sLFy4Yf5Nm9wKzSJs2bTR9+nTNnz9f8+fP19SpU9W5c2eNHz++3ELHNLsWmPn5+a6bZRRp0KCB9u3bV26Jk5+fr9zcXNWrV6+6Rq0wOxSY0dHRSkhI0O7duzV58mQ99thj6tixowYNGqTU1FR9/fXXOnr0qO6///5iN9FYtGiR6zqr5R3hWNXsXmBWxg033KDY2FjFxsbq3//+t77//nutXbvW9FjlcscCs127djp8+LA++ugjvfDCC/Lz81NeXp4++ugjWZalkJAQzZo1S23atCn2envu3DnNnDlTlmUZ/Z2xc4EZERGhFStWKC4uTs8//3yJ9wN5eXn65JNPZFlWiVM1z507p5ycHKPvb+xcYEZGRmr58uV67733XL/3FXHu3Dm99957sixLHTt2rOIpy2bXArN58+bavn278vLyyjwjoyzZ2dlVNFXFeUKBeSkKQFTYPffco/Xr17suIjpv3jy1a9eu3Lvo7ty5U3l5eXI6nbr99tuNlld2LjCbN2+uxMRE1wuq3di1wGzZsqX279+vDRs2XLEAlKQ+ffrI29tb7733npYuXSqn02n0kHE7F5g33XST9u/fr/T0dHXq1KnYshdffLHcbffv36/CwsJKvzm6luxeYF7K29tbQ4cOVbdu3fT+++8rNTVV27ZtU2xsrNvdPOZydiswc3Jyyvz9TktLU2RkZKnL9u3bp8LCwnKP9q0Odi0wLcvSyy+/rBdffFEHDhzQq6++qsDAQIWFhbmO2k1OTlZycnKJbZ1Op4YNG6b27dtX89T/YfcC82q1bdtWbdu21fjx402PUmHuUmDed999WrVqlbZv365HH31UTZo00eHDh5WXl6egoCD98Y9/1KRJk/Tkk08qOjradRfd5ORkHT9+XJZlGX3+t3OBOWjQICUmJmrTpk2aMGGC+vbtq6ZNm6qwsFAHDhxQQkKCsrKy5OXlVeK6qUX7AaGhoSZGl2TvAnPw4MFKTk7W9u3b9eSTT6pv377q2LGjQkNDS/wc+fn5OnDggDZt2qSlS5cqMzNTvr6+xq69KNm3wGzevLm2bdtm2/1YuxeYl6MARKU8//zz+uKLL/Ttt98qNzdXqamp+vHHH0tdt2gHoFatWoqJidGwYcOqc9QS7FxghoeHS7r4yWZlS6VatWqpR48eRosouxaYHTt21PLly7V8+XI98MADFcrw7rvvlre3t6ZPn278CEA7F5gtWrRQYmKidu7cWaIAvJI1a9ZI0lVd7/NasXuBWZqmTZvqb3/7m5YtW6ZZs2bps88+08qVK02PdUV2KjDLe85ITk4uswD84YcfJMn4UVx2LjBr166tv//97/rss8+0dOlSnTlzRtu2bSt2x+LLNWzYUGPGjFG3bt2qe9xi7F5g/rdMXl/6v2GywLzxxhv1+OOPuz4UT09Pl3Qxyz/+8Y8KCwvT0KFD9eWXX2rRokWu7Yr+DmJiYoy+xtq5wAwJCdHTTz+td955R8ePH9dnn31WbLnT6ZSXl5eeeuopNWnSpNiylJQUScWvJ1zd7FxgNm7cWC+++KKmTp2qzMxMff755/r8888lXTzK69J9wUtPlXU6nQoICNDzzz9v9L2ZXQvMov3YoucZu7F7gXk5CkBUipeXl0aOHKn7779fa9eu1bZt25SRkaFTp04pLy9P0sXrPtSvX1/NmjVTRESEunbt6jbXlrFrgdm2bdtib8Aqw9fXVxMnTry2A1WSXQvMzp07KyIiQoWFhUpLS1Pr1q0rtF10dLRq1aqlTz/9tIonLJ+dC8wePXqoRYsWpZ4+Up6CggIdPHhQ7du3L/MmG9XB7gVmeXr37q3bbrtNcXFxbn/q3aXcvcC82ud4STp8+LDat2+vzp07X8OJKs/uBaaPj4/Gjh2r2NhYrVmzRj/99JN+/fVXnT17Vk6n0/X+JjQ0VB06dFD79u2N3gjhUnYtMHv06FHuTUquB6YKzN69e6tdu3ZKSUnRyZMn1ahRI0VHR7uK+KFDh6pJkyb69ttvlZGRoRo1aigsLEz9+vUzXnrbvcDs3r27mjZtqnnz5mnr1q06d+6cpIvv2W+99VYNGTKk1MsOPf7443r88cere9xi7F5gRkRE6MMPP9T8+fO1evVqnTp1StLFUzVLu2NrvXr11L17dw0ePNj4/qxdC8x27dpp6NChsiyr0vuBQUFBmjlzZhVOd2V2LzAvZzlNH6ICGJCVlWXLAhOojPz8fL366qsqLCzUyJEjK1xgStLatWtdBabpF147ysnJ0cGDBxUUFFTiDXB5CgoK9OabbyonJ0ePPvpomXcxdBc7duzQ0aNHJbnvXXRLc+rUqWIFpmVZWrhwodmhrmNvvvmmsrOzNWTIkBJ3zkblnTlzxnYFpl298847CggI0Lhx40yPcl06ePBgmQWmdPGDB3csMC9VWFio7OxsWZaloKAg2/xN7t27t9IFpjs6cOBAufuCFTmDprplZWWVKDDL4k4Fpl2dOnVKS5culWVZio2NrVSB6XQ6dfz4cUlym0vPUADiqs2dO1fR0dGu68mg+pC9GeRuDtmb48nZu3OB6cm5uzuyN4fszSB3c+yevV0LTE9hxwITZlEA4qrFxMTIsiyFhYUpOjpa3bt3d8s7EHoisjeD3M0he3PI3gxyN4fszSF7M8jdHLIHUJ0oAHHVJk2apL1790q6ePqUZVmKiIhQVFSUunbtWuE7E5li50/cyN4McjeH7M0hezPI3RyyN4fszSB3c8geQHWiAMR/5eDBg0pMTNTq1at15MgRSRdfvGrWrKnbbrtNUVFR6tSpk7y8vAxPWpLdP3EjezPI3RyyN4fszSB3c8jeHLI3g9zNIXv3d+7cOc2YMUOWZenpp582Pc51g9yvPQpAXDNpaWlKSkpSSkqKsrKyJF188QoMDFS3bt0UFRWlNm3aGJ7yP+z+idulyN4McjeH7M0hezPI3RyyN4fszSB3c8jePWVnZ2vkyJG2vWmYXYs0cr/2KABxzRUWFmrr1q1KTEzU+vXrlZeX57pbTnBwsOLi4gxP+B92/sStNGRvBrmbQ/bmkL0Z5G4O2ZtD9maQuzlk717sXkTZdX67zl3EHeenAESVOn/+vDZs2KD4+Hjt2bPHrX75L2e3T9yuhOzNIHdzyN4csjeD3M0he3PI3gxyN4fszXPHIqcy7Dq/Xecu4o7zUwCiymRnZyslJUXJycnatWuXnE6nW/3yl8VOn7iVhezNIHdzyN4csjeD3M0he3PI3gxyN4fsr53p06df9bYXLlxQcnKyLbIvjckiitzdqwD0Nj0APIvD4dD69euVlJSkzZs3q7CwUEUd80033aSoqCjDE15ZjRo1FBkZqcjIyBKfuB07dsz0eGUiezPI3RyyN4fszSB3c8jeHLI3g9zNIfuqsWrVKlcJaUf/bZFmCrm7FwpA/NcKCwu1ZcsWJSUl6YcffpDD4XC9SDVs2FDdu3dXdHS0QkNDDU9aOZd+4lZ0cVt3Q/ZmkLs5ZG8O2ZtB7uaQvTlkbwa5m0P2Va+ohGrdurVq165dqW0vXLigjRs3VsVYFWbXIo3c3QsFIK5aWlqakpOTtXr1amVnZ0uSnE6nAgMDdeeddyo6Olpt27Y1PGXl2OUTN7I3g9zNIXtzyN4McjeH7M0hezPI3Ryyrz4hISE6dOiQ7rnnHvXq1atS2xadymmSXYs0cjdbYF6OAhBXbfLkybIsS06ns8Rdnry97fOrZcdP3MjeDHI3h+zNIXszyN0csjeH7M0gd3PIvvqEh4fr0KFD2rNnT6WLKHdg1yKN3M0WmJezz7MK3I5lWYqIiFBUVJS6du0qf39/0yNVip0/cSN7M8jdHLI3h+zNIHdzyN4csjeD3M0h++rTvHlzJSYmas+ePaZHuSp2LdLI3b1QAOKqffrpp6pXr57pMa6anT9xI3szyN0csjeH7M0gd3PI3hyyN4PczSH76hMeHi5J+uWXX1x3U66oWrVqqUePHkavBWfXIo3c3Yt7/VXCVuz8YiXZ+xM3sjeD3M0he3PI3gxyN4fszSF7M8jdHLKvPm3bttWiRYuualtfX19NnDjx2g5USXYt0sjdbIF5OctZdHI+cJ05deqU7V907YrszSB3c8jeHLI3g9zNIXtzyN4McjeH7AF7oQAEJOXn52vLli1KT09XVlaWJKlOnToKDw9Xhw4dVLNmTcMTei6yN4PczSF7c8jeDHI3h+zNIXszyN0cu2U/d+5cRUdHq2nTpqZHua6Qu1kUgLjufffdd5o3b57Onj1b6vKgoCANGzZM/fr1q+bJPB/Zm0Hu5pC9OWRvBrmbQ/bmkL0Z5G6OHbOPiYmRZVkKCwtTdHS0unfvztGM1YDczaIAxHUtLi5OCQkJrvP5mzVrpgYNGkiSMjMzlZGR4VrWr18/jR8/3vDEnoPszSB3c8jeHLI3g9zNIXtzyN4McjfHrtlPmjRJe/fulXTxWoaXX8/Qz8/P8ITls+uRdORuFgUgrlubNm3SlClTJEm9e/dWbGys68WqyMmTJ/Xll19q+fLlsixLf/nLXxQZGWliXI9C9maQuzlkbw7Zm0Hu5pC9OWRvBrmbY/fsDx48qMTERK1evVpHjhyRdLGUuvyOxl5eXoYnLcnOR9KRuzncBRjXrSVLlsiyLMXExOiRRx4pdZ369evrySeflL+/vxYuXKglS5a4zQuWnZG9GeRuDtmbQ/ZmkLs5ZG8O2ZtB7ubYPfumTZtqxIgRGjFihNLS0pSUlKSUlBRlZWUpJSVFa9asUWBgoLp166aoqCi1adPG9MguN998s/bu3au9e/fql19+0axZs2xzJB25m1PD9ACAKbt375ZlWRoyZMgV1x0yZIgsy9KuXbuqYTLPR/ZmkLs5ZG8O2ZtB7uaQvTlkbwa5m+NJ2bdq1Urjx4/XrFmz9Morryg6Olq+vr46c+aMli5dqueff95tTl+WpLffflvvv/++hgwZokaNGqmwsFBbtmzRu+++q1GjRumtt97Shg0bVFBQYHrUcpF79eIIQFy3zpw5I39/fwUGBl5x3cDAQAUEBJR5YVtUDtmbQe7mkL05ZG8GuZtD9uaQvRnkbo4nZl+jRg1FRkYqMjJS58+f14YNGxQfH689e/bo2LFjpscrxs5H0l2O3KsHBSCuW0FBQcrOzlZOTo4CAgLKXffs2bPKyclR7dq1q2k6z0b2ZpC7OWRvDtmbQe7mkL05ZG8GuZvjydlnZ2crJSVFycnJrptWuLNWrVqpVatWGjt2rLZu3arExEStX7/edSTd0qVLFRwcrLi4ONOjlovcqxanAOO61aJFCzmdTsXHx19x3fj4eDmdTrVs2bIaJvN8ZG8GuZtD9uaQvRnkbg7Zm0P2ZpC7OZ6WvcPhUHJysl577TWNGTNGcXFx+umnn+R0OnXTTTdp1KhRpke8oqIj6SZNmqQ5c+bo2Wef1c033yyn0+l2R9IVIffqwxGAuG716dNHqampWrBggRwOhx588EHVrVu32DqnT5/W119/re+++06WZalv375mhvUwZG8GuZtD9uaQvRnkbg7Zm0P2ZpC7OZ6QfdE13JKSkvTDDz/I4XDI6XRKkho2bKju3bsrOjpaoaGhhietHHc/ko7czbCcRSkD16EPPvhAy5Ytk2VZqlGjhpo1a+a6dX1mZqYyMjJUWFgop9OpPn36aMKECYYn9hxkbwa5m0P25pC9GeRuDtmbQ/ZmkLs5ds0+LS1NycnJWr16tbKzsyVJTqdTgYGBuvPOOxUdHa22bdsanrJyHA6H1q9fr6SkJG3evNmVuyTddNNNioqK0gMPPGB0RnI3iwIQ172FCxfqq6++Uk5OTqnLAwMD9dBDDykmJqaaJ/N8ZG8GuZtD9uaQvRnkbg7Zm0P2ZpC7OXbMPiYmRpZlyel0qmbNmrrtttsUFRWlTp06ydvbPidK2u1IOnI3iwIQ0MXWfvPmzdqzZ4+ysrIkSXXq1FF4eLg6dOggHx8fwxN6LrI3g9zNIXtzyN4McjeH7M0hezPI3Ry7ZT9w4EBFREQoKipKXbt2lb+/v+mRKsWuR9KRu1kUgAAAAAAA4Lpx6tQp1atXz/QYV82uR9KRu1nuPyEAAAAAAMA1YucSSpIsy7LlkXTkbhZHAAIAAAAAANiE3Y+ksyu7504BCAAAAAAAYEP5+fnasmWL0tPTS73+Ys2aNQ1P6JnsmDunAAMAAAAAANjMd999p3nz5uns2bOlLg8KCtKwYcPUr1+/ap7Ms9k1d44ABAAAAAAAsJG4uDglJCTI6XTKsiw1a9ZMDRo0kCRlZmYqIyPDtaxfv34aP3684Yk9g51z5whAAAAAAAAAm9i0aZMWL14sSerdu7diY2NdJVSRkydP6ssvv9Ty5cuVkJCgLl26KDIy0sS4HsPuudcwPQAAAAAAAAAqZsmSJbIsSwMHDtQTTzxRooSSpPr16+vJJ5/UwIED5XQ6tWTJEgOTeha7504BCAAAAAAAYBO7d++WZVkaMmTIFdcdMmSILMvSrl27qmEyz2b33CkAAQAAAAAAbOLMmTPy9/dXYGDgFdcNDAxUQEBAmTesQMXZPXcKQAAAAAAAAJsICgpSbm6ucnJyrrju2bNnlZOTU6HSCuWze+4UgAAAAAAAADbRokULOZ1OxcfHX3Hd+Ph4OZ1OtWzZshom82x2z50CEAAAAAAAwCb69Okjp9OpBQsW6KOPPtLp06dLrHP69GnNnDlTCxYskGVZ6tu3b/UP6mHsnrvldDqdpocAAAAAAABAxXzwwQdatmyZLMtSjRo11KxZM9ddaTMzM5WRkaHCwkI5nU716dNHEyZMMDyxZ7Bz7hSAAAAAAAAANrNw4UJ99dVXZV6TLjAwUA899JBiYmKqeTLPZtfcKQABAAAAAABsyOFwaPPmzdqzZ4+ysrIkSXXq1FF4eLg6dOggHx8fwxN6JjvmTgEIAAAAAAAAeDBuAgIAAAAAAAB4MApAAAAAAAAAwINRAAIAAAAAAAAejAIQAAAAAAAA8GAUgAAAAAAAAIAHowAEAAAAAAAAPBgFIAAAAAAAAODBKAABAAAAAAAAD0YBCAAAAAAAAHgwCkAAAAAAAADAg1EAAgAA4Krt27dPlmXJsiyNGTPG9DgAAAAoBQUgAAAAAAAA4MEoAAEAAAAAAAAPRgEIAAAAAAAAeDAKQAAAAAAAAMCDUQACAAAAAAAAHowCEAAAAFVq9+7dmjZtmgYNGqQWLVooMDBQtWrVUnBwsLp3767XX39dJ06cKHP7IUOGuO40vGbNmgr9mz169HBt89NPP5W53r/+9S+NGjVK4eHhCgoKkr+/v8LCwjRixAitXLmy3H8jMTHR9W+88sorkqSff/5ZzzzzjNq1a6e6desWWwYAAGCKt+kBAAAA4LnmzJmj0aNHl7rs+PHjOn78uFavXq233npLX3zxhfr3719ivQkTJmj+/PmSpLi4ON15553l/ptpaWlKTEyUJHXv3l1t2rQpsU5GRoZiY2O1bt26Esv27dunffv26fPPP9fgwYM1Z84c+fv7X+lH1dy5czV+/Hjl5eVdcV0AAIDqRAEIAACAKpObmyvLsnTrrbeqe/fuat26terXry9JOnjwoFauXKmlS5cqOztbgwcP1tq1a9WxY8dij9GzZ0+1atVKaWlp+vrrrzV9+nTVrVu3zH8zLi7O9fXjjz9eYnlGRoZuv/12/frrr5KkyMhIDRw4UOHh4apRo4bS0tI0Z84c7d27VwsWLFBOTo4SEhJkWVaZ/+batWv1xhtvyLIsjR49WnfddZcCAgKUnp6u0NDQykQGAABwzVlOp9NpeggAAADY0759+xQWFiZJGj16tGbNmlVs+c6dO+Xj46Pw8PAyH2PlypWKiYlRbm6uevXqVeqpt++8844mTZokSXr33Xf1+9//vtTHcjgcCgkJUWZmpho0aKBDhw7Jx8fHtdzpdOrOO+/UunXr5OXlpQ8//FDjxo0r9XHGjBmjefPmSZJmzpypsWPHFlsnMTFRPXr0cP13cHCwVqxYoYiIiDJ/VgAAABO4BiAAAACqTLt27cot/yTp7rvv1h/+8AdJ0vfff69Dhw6VWGfMmDGu03AvPcLvcgsWLFBmZqZrm0vLP+niNf+KTvt95ZVXSi3/JMnHx0ezZ8/WTTfdJEn6xz/+Ue7PIEkzZsyg/AMAAG6JAhAAAADGdevWzfX1Dz/8UGJ53bp1NXToUEnSjh07tHbt2lIf59JycPz48SWWz549W9LFgu/pp58ud6ZatWpp2LBhkqRdu3bpwIEDZa574403KiYmptzHAwAAMIVrAAIAAKDKpaSk6Msvv9SGDRu0d+9enTlzRvn5+aWue/DgwVK/P2HCBH3yySeSLhZ9d9xxR7HlaWlpSkpKknTxLsAtW7Ys8RjJycmSpEaNGmnVqlVXnPvUqVOur//973+XeT2/O++8s9xrBAIAAJhEAQgAAIAqc/bsWY0YMUKLFi2q8DbZ2dmlfr9z587q0qWLUlNT9dVXX2n69OmqU6eOa/mVbv6Rk5OjEydOSJIOHDigQYMGVXgmSTp58mSZy5o2bVqpxwIAAKhOFIAAAACoMrGxsUpISJAkBQQEqH///oqMjFSTJk3k7+8vb++Lb0d37Nihl19+WZJUUFBQ5uNNmDBBqampysvL02effaannnpK0sWbdhSd3tuwYcNSy73Tp0//Vz/L+fPny1zm5+f3Xz02AABAVaIABAAAQJVYs2aNq/y75ZZbtHz5cjVu3LjUdWvWrFmhxxw6dKieeeYZnTp1SnFxca4C8NKbfzzyyCOqVatWiW0DAwNdX3fs2FEbN26s1M8DAABgV9wEBAAAAFVi+fLlrq//+te/lln+SdIvv/xSocf08/PTmDFjJEnbt2933dF3xowZkiTLskq9+Yck1alTx1UClnWdQQAAAE9EAQgAAIAqceTIEdfX4eHh5a67ZMmSCj/u7373O9cNN+Li4rRr1y7XzT169eql5s2bl7ltVFSUJOnYsWMcAQgAAK4bFIAAAACoEgEBAa6v09PTy1xv3bp1lSoAW7ZsqZ49e0qSvvrqK7311luuZaXd/ONSo0ePdn390ksvyel0VvjfBQAAsCsKQAAAAFSJLl26uL6eMmWKzp07V2Kdbdu26cEHH6x0EffEE09IknJzc/XJJ59Ikho1aqSYmJhyt3vwwQd1++23S5KWLl2qUaNG6ezZs2WuX1BQoKVLl+r111+v1HwAAADuhJuAAAAAoEo88MADCg0N1YEDB/Tjjz+qVatWGjt2rMLDw5Wbm6ukpCTNmzdP+fn5Gj16tOsuvhVx//33q0mTJjp8+LDre48++ugVbyZiWZYWLFigrl27KiMjQ3PnztXixYs1ZMgQderUSfXr19e5c+d0+PBhbd26VStWrNDx48fVq1cvvfTSS1edBQAAgEkUgAAAAKgSPj4+io+PV58+fXTixAkdOHBAf/7zn4ut4+XlpalTp+r222+vVAHo7e2tcePGacqUKZIuFnvjxo2r0LYhISH68ccfNWbMGC1ZssR1R+HyNG3atMKzAQAAuBtOAQYAAECV6dSpk7Zt26ZnnnlGrVq1kq+vrwIDA9WyZUs9/vjj2rBhg/70pz9d1WPfe++9xb4OCwur8LbBwcFKSEjQunXr9NRTT6lDhw5q0KCBvLy8FBAQoObNm2vAgAGaOnWqduzYoVmzZl3VjAAAAO7AcnLlYwAAANjQxIkTNX36dElSfHy8Bg0aZHgiAAAA90QBCAAAANvJyclRs2bNdOrUKYWEhGjfvn3y9ubqNgAAAKXhFGAAAADYzrRp03Tq1ClJ0lNPPUX5BwAAUA6OAAQAAIDbO3TokLZv3668vDwlJSXpvffeU0FBgRo1aqT09HQFBgaaHhEAAMBt8VEpAAAA3N6KFSv0yCOPFPuel5eXPv74Y8o/AACAK+AUYAAAANhKo0aN1LdvX61evVr9+/c3PQ4AAIDb4xRgAAAAAAAAwINxBCAAAAAAAADgwSgAAQAAAAAAAA9GAQgAAAAAAAB4MApAAAAAAAAAwINRAAIAAAAAAAAejAIQAAAAAAAA8GAUgAAAAAAAAIAHowAEAAAAAAAAPBgFIAAAAAAAAODBKAABAAAAAAAAD0YBCAAAAAAAAHgwCkAAAAAAAADAg1EAAgAAAAAAAB6MAhAAAAAAAADwYBSAAAAAAAAAgAejAAQAAAAAAAA8GAUgAAAAAAAA4MEoAAEAAAAAAAAPRgEIAAAAAAAAeDAKQAAAAAAAAMCDUQACAAAAAAAAHowCEAAAAAAAAPBgFIAAAAAAAACAB6MABAAAAAAAADwYBSAAAAAAAADgwf4/TjteHUsZ1F8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 640
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "filtered = df\n",
    "filtered = filtered[filtered[\"pos\"] == 4]\n",
    "g = (\n",
    "    ggplot(filtered)\n",
    "    + geom_bar(aes(x=\"layer\", y=\"prob\", fill=\"token\"), stat=\"identity\")\n",
    "    + theme(axis_text_x=element_text(rotation=90), legend_position=\"none\")\n",
    "    + scale_y_log10()\n",
    "    + facet_wrap(\"~token\", ncol=1)\n",
    ")\n",
    "print(g)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
